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Diagnostic proficiency test using digital cytopathology and comparative assessment of whole slide images of cytologic samples for quality assurance program in Korea
Yosep Chong, Soon Auck Hong, Hoon Kyu Oh, Soo Jin Jung, Bo-Sung Kim, Ji Yun Jeong, Ho-Chang Lee, Gyungyub Gong
J Pathol Transl Med. 2023;57(5):251-264.   Published online August 24, 2023
DOI: https://doi.org/10.4132/jptm.2023.07.17
  • 4,308 View
  • 311 Download
  • 4 Web of Science
  • 4 Crossref
AbstractAbstract PDFSupplementary Material
Background
The Korean Society for Cytopathology introduced a digital proficiency test (PT) in 2021. However, many doubtful opinions remain on whether digitally scanned images can satisfactorily present subtle differences in the nuclear features and chromatin patterns of cytological samples.
Methods
We prepared 30 whole-slide images (WSIs) from the conventional PT archive by a selection process for digital PT. Digital and conventional PT were performed in parallel for volunteer institutes, and the results were compared using feedback. To assess the quality of cytological assessment WSIs, 12 slides were collected and scanned using five different scanners, with four cytopathologists evaluating image quality through a questionnaire.
Results
Among the 215 institutes, 108 and 107 participated in glass and digital PT, respectively. No significant difference was noted in category C (major discordance), although the number of discordant cases was slightly higher in the digital PT group. Leica, 3DHistech Pannoramic 250 Flash, and Hamamatsu NanoZoomer 360 systems showed comparable results in terms of image quality, feature presentation, and error rates for most cytological samples. Overall satisfaction was observed with the general convenience and image quality of digital PT.
Conclusions
As three-dimensional clusters are common and nuclear/chromatin features are critical for cytological interpretation, careful selection of scanners and optimal conditions are mandatory for the successful establishment of digital quality assurance programs in cytology.

Citations

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  • Sensitivity, Specificity, and Cost–Benefit Effect Between Primary Human Papillomavirus Testing, Primary Liquid‐Based Cytology, and Co‐Testing Algorithms for Cervical Lesions
    Chang Gok Woo, Seung‐Myoung Son, Hye‐Kyung Hwang, Jung‐Sil Bae, Ok‐Jun Lee, Ho‐Chang Lee
    Diagnostic Cytopathology.2025; 53(1): 35.     CrossRef
  • Integration of AI‐Assisted in Digital Cervical Cytology Training: A Comparative Study
    Yihui Yang, Dongyi Xian, Lihua Yu, Yanqing Kong, Huaisheng Lv, Liujing Huang, Kai Liu, Hao Zhang, Weiwei Wei, Hongping Tang
    Cytopathology.2025; 36(2): 156.     CrossRef
  • Validation of digital image slides for diagnosis in cervico-vaginal cytology
    Francisco Tresserra, Gemma Fabra, Olga Luque, Miriam Castélla, Carla Gómez, Carmen Fernández-Cid, Ignacio Rodríguez
    Revista Española de Patología.2024; 57(3): 182.     CrossRef
  • Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology
    Yujin Lee, Mohammad Rizwan Alam, Hongsik Park, Kwangil Yim, Kyung Jin Seo, Gisu Hwang, Dahyeon Kim, Yeonsoo Chung, Gyungyub Gong, Nam Hoon Cho, Chong Woo Yoo, Yosep Chong, Hyun Joo Choi
    Thyroid®.2024; 34(6): 723.     CrossRef
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Postmortem lung and heart examination of COVID-19 patients in a case series from Jordan
Maram Abdaljaleel, Isra Tawalbeh, Malik Sallam, Amjad Bani Hani, Imad M. Al-Abdallat, Baheth Al Omari, Sahar Al-Mustafa, Hasan Abder-Rahman, Adnan Said Abbas, Mahmoud Zureigat, Mousa A. Al-Abbadi
J Pathol Transl Med. 2023;57(2):102-112.   Published online March 14, 2023
DOI: https://doi.org/10.4132/jptm.2023.01.30
  • 3,474 View
  • 149 Download
AbstractAbstract PDF
Background
Coronavirus disease 2019 (COVID-19) has emerged as a pandemic for more than 2 years. Autopsy examination is an invaluable tool to understand the pathogenesis of emerging infections and their consequent mortalities. The aim of the current study was to present the lung and heart pathological findings of COVID-19–positive autopsies performed in Jordan.
Methods
The study involved medicolegal cases, where the cause of death was unclear and autopsy examination was mandated by law. We included the clinical and pathologic findings of routine gross and microscopic examination of cases that were positive for COVID-19 at time of death. Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was confirmed through molecular detection by real-time polymerase chain reaction, serologic testing for IgM and electron microscope examination of lung samples.
Results
Seventeen autopsies were included, with male predominance (76.5%), Jordanians (70.6%), and 50 years as the mean age at time of death. Nine out of 16 cases (56.3%) had co-morbidities, with one case lacking such data. Histologic examination of lung tissue revealed diffuse alveolar damage in 13/17 cases (76.5%), and pulmonary microthrombi in 8/17 cases (47.1%). Microscopic cardiac findings were scarcely detected. Two patients died as a direct result of acute cardiac disease with limited pulmonary findings.
Conclusions
The detection of SARS-CoV-2 in postmortem examination can be an incidental or contributory finding which highlights the value of autopsy examination to determine the exact cause of death in controversial cases.
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Development of quality assurance program for digital pathology by the Korean Society of Pathologists
Yosep Chong, Jeong Mo Bae, Dong Wook Kang, Gwangil Kim, Hye Seung Han
J Pathol Transl Med. 2022;56(6):370-382.   Published online November 15, 2022
DOI: https://doi.org/10.4132/jptm.2022.09.30
  • 3,798 View
  • 141 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
Background
Digital pathology (DP) using whole slide imaging is a recently emerging game changer technology that can fundamentally change the way of working in pathology. The Digital Pathology Study Group (DPSG) of the Korean Society of Pathologists (KSP) published a consensus report on the recommendations for pathologic practice using DP. Accordingly, the need for the development and implementation of a quality assurance program (QAP) for DP has been raised.
Methods
To provide a standard baseline reference for internal and external QAP for DP, the members of the Committee of Quality Assurance of the KSP developed a checklist for the Redbook and a QAP trial for DP based on the prior DPSG consensus report. Four leading institutes participated in the QAP trial in the first year, and we gathered feedback from these institutes afterwards.
Results
The newly developed checklists of QAP for DP contain 39 items (216 score): eight items for quality control of DP systems; three for DP personnel; nine for hardware and software requirements for DP systems; 15 for validation, operation, and management of DP systems; and four for data security and personal information protection. Most participants in the QAP trial replied that continuous education on unfamiliar terminology and more practical experience is demanding.
Conclusions
The QAP for DP is essential for the safe implementation of DP in pathologic practice. Each laboratory should prepare an institutional QAP according to this checklist, and consecutive revision of the checklist with feedback from the QAP trial for DP needs to follow.

Citations

Citations to this article as recorded by  
  • An equivalency and efficiency study for one year digital pathology for clinical routine diagnostics in an accredited tertiary academic center
    Viola Iwuajoku, Kübra Ekici, Anette Haas, Mohammed Zaid Khan, Azar Kazemi, Atsuko Kasajima, Claire Delbridge, Alexander Muckenhuber, Elisa Schmoeckel, Fabian Stögbauer, Christine Bollwein, Kristina Schwamborn, Katja Steiger, Carolin Mogler, Peter J. Schüf
    Virchows Archiv.2025;[Epub]     CrossRef
  • Diagnostic proficiency test using digital cytopathology and comparative assessment of whole slide images of cytologic samples for quality assurance program in Korea
    Yosep Chong, Soon Auck Hong, Hoon Kyu Oh, Soo Jin Jung, Bo-Sung Kim, Ji Yun Jeong, Ho-Chang Lee, Gyungyub Gong
    Journal of Pathology and Translational Medicine.2023; 57(5): 251.     CrossRef
Reviews
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Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists
Yosep Chong, Dae Cheol Kim, Chan Kwon Jung, Dong-chul Kim, Sang Yong Song, Hee Jae Joo, Sang-Yeop Yi
J Pathol Transl Med. 2020;54(6):437-452.   Published online October 8, 2020
DOI: https://doi.org/10.4132/jptm.2020.08.27
  • 8,882 View
  • 313 Download
  • 19 Web of Science
  • 23 Crossref
AbstractAbstract PDFSupplementary Material
Digital pathology (DP) using whole slide imaging (WSI) is becoming a fundamental issue in pathology with recent advances and the rapid development of associated technologies. However, the available evidence on its diagnostic uses and practical advice for pathologists on implementing DP remains insufficient, particularly in light of the exponential growth of this industry. To inform DP implementation in Korea, we developed relevant and timely recommendations. We first performed a literature review of DP guidelines, recommendations, and position papers from major countries, as well as a review of relevant studies validating WSI. Based on that information, we prepared a draft. After several revisions, we released this draft to the public and the members of the Korean Society of Pathologists through our homepage and held an open forum for interested parties. Through that process, this final manuscript has been prepared. This recommendation contains an overview describing the background, objectives, scope of application, and basic terminology; guidelines and considerations for the hardware and software used in DP systems and the validation required for DP implementation; conclusions; and references and appendices, including literature on DP from major countries and WSI validation studies.

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    Viola Iwuajoku, Kübra Ekici, Anette Haas, Mohammed Zaid Khan, Azar Kazemi, Atsuko Kasajima, Claire Delbridge, Alexander Muckenhuber, Elisa Schmoeckel, Fabian Stögbauer, Christine Bollwein, Kristina Schwamborn, Katja Steiger, Carolin Mogler, Peter J. Schüf
    Virchows Archiv.2025;[Epub]     CrossRef
  • An adapted & improved validation protocol for digital pathology implementation
    Ying-Han R. Hsu, Iman Ahmed, Juliana Phlamon, Charlotte Carment-Baker, Joyce Yin Tung Chan, Ioannis Prassas, Karen Weiser, Shaza Zeidan, Blaise Clarke, George M. Yousef
    Seminars in Diagnostic Pathology.2025; 42(4): 150905.     CrossRef
  • Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
    Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J.V. Campbell, Andrew P. Norgan, Cynthia Lokker
    Journal of Pathology Informatics.2024; 15: 100348.     CrossRef
  • Swiss digital pathology recommendations: results from a Delphi process conducted by the Swiss Digital Pathology Consortium of the Swiss Society of Pathology
    Andrew Janowczyk, Inti Zlobec, Cedric Walker, Sabina Berezowska, Viola Huschauer, Marianne Tinguely, Joel Kupferschmid, Thomas Mallet, Doron Merkler, Mario Kreutzfeldt, Radivoje Gasic, Tilman T. Rau, Luca Mazzucchelli, Isgard Eyberg, Gieri Cathomas, Kirst
    Virchows Archiv.2024; 485(1): 13.     CrossRef
  • ChatGPT as an aid for pathological diagnosis of cancer
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    Pathology - Research and Practice.2024; 253: 154989.     CrossRef
  • Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology
    Durre Aden, Sufian Zaheer, Sabina Khan
    Revista Española de Patología.2024; 57(3): 198.     CrossRef
  • Remote Placental Sign-Out: What Digital Pathology Can Offer for Pediatric Pathologists
    Casey P. Schukow, Jacqueline K. Macknis
    Pediatric and Developmental Pathology.2024; 27(4): 375.     CrossRef
  • Digital Validation in Breast Cancer Needle Biopsies: Comparison of Histological Grade and Biomarker Expression Assessment Using Conventional Light Microscopy, Whole Slide Imaging, and Digital Image Analysis
    Ji Eun Choi, Kyung-Hee Kim, Younju Lee, Dong-Wook Kang
    Journal of Personalized Medicine.2024; 14(3): 312.     CrossRef
  • Pathologists light level preferences using the microscope—study to guide digital pathology display use
    Charlotte Jennings, Darren Treanor, David Brettle
    Journal of Pathology Informatics.2024; 15: 100379.     CrossRef
  • Eye tracking in digital pathology: A comprehensive literature review
    Alana Lopes, Aaron D. Ward, Matthew Cecchini
    Journal of Pathology Informatics.2024; 15: 100383.     CrossRef
  • Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer
    Young-Gon Kim, In Hye Song, Seung Yeon Cho, Sungchul Kim, Milim Kim, Soomin Ahn, Hyunna Lee, Dong Hyun Yang, Namkug Kim, Sungwan Kim, Taewoo Kim, Daeyoung Kim, Jonghyeon Choi, Ki-Sun Lee, Minuk Ma, Minki Jo, So Yeon Park, Gyungyub Gong
    Cancer Research and Treatment.2023; 55(2): 513.     CrossRef
  • Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers
    Mohammad Rizwan Alam, Kyung Jin Seo, Jamshid Abdul-Ghafar, Kwangil Yim, Sung Hak Lee, Hyun-Jong Jang, Chan Kwon Jung, Yosep Chong
    Briefings in Bioinformatics.2023;[Epub]     CrossRef
  • Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries
    Sumi Piya, Jochen K. Lennerz
    Frontiers in Medicine.2023;[Epub]     CrossRef
  • Diagnostic proficiency test using digital cytopathology and comparative assessment of whole slide images of cytologic samples for quality assurance program in Korea
    Yosep Chong, Soon Auck Hong, Hoon Kyu Oh, Soo Jin Jung, Bo-Sung Kim, Ji Yun Jeong, Ho-Chang Lee, Gyungyub Gong
    Journal of Pathology and Translational Medicine.2023; 57(5): 251.     CrossRef
  • Real-World Implementation of Digital Pathology: Results From an Intercontinental Survey
    Daniel Gomes Pinto, Andrey Bychkov, Naoko Tsuyama, Junya Fukuoka, Catarina Eloy
    Laboratory Investigation.2023; 103(12): 100261.     CrossRef
  • National digital pathology projects in Switzerland: A 2023 update
    Rainer Grobholz, Andrew Janowczyk, Ana Leni Frei, Mario Kreutzfeldt, Viktor H. Koelzer, Inti Zlobec
    Die Pathologie.2023; 44(S3): 225.     CrossRef
  • Understanding the ethical and legal considerations of Digital Pathology
    Cheryl Coulter, Francis McKay, Nina Hallowell, Lisa Browning, Richard Colling, Philip Macklin, Tom Sorell, Muhammad Aslam, Gareth Bryson, Darren Treanor, Clare Verrill
    The Journal of Pathology: Clinical Research.2022; 8(2): 101.     CrossRef
  • Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape
    Muhammad Joan Ailia, Nishant Thakur, Jamshid Abdul-Ghafar, Chan Kwon Jung, Kwangil Yim, Yosep Chong
    Cancers.2022; 14(10): 2400.     CrossRef
  • Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review
    Mohammad Rizwan Alam, Jamshid Abdul-Ghafar, Kwangil Yim, Nishant Thakur, Sung Hak Lee, Hyun-Jong Jang, Chan Kwon Jung, Yosep Chong
    Cancers.2022; 14(11): 2590.     CrossRef
  • Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
    Jiyoon Jung, Eunsu Kim, Hyeseong Lee, Sung Hak Lee, Sangjeong Ahn
    Applied Sciences.2022; 12(18): 9159.     CrossRef
  • Development of quality assurance program for digital pathology by the Korean Society of Pathologists
    Yosep Chong, Jeong Mo Bae, Dong Wook Kang, Gwangil Kim, Hye Seung Han
    Journal of Pathology and Translational Medicine.2022; 56(6): 370.     CrossRef
  • Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence
    Young Sin Ko, Yoo Mi Choi, Mujin Kim, Youngjin Park, Murtaza Ashraf, Willmer Rafell Quiñones Robles, Min-Ju Kim, Jiwook Jang, Seokju Yun, Yuri Hwang, Hani Jang, Mun Yong Yi, Anwar P.P. Abdul Majeed
    PLOS ONE.2022; 17(12): e0278542.     CrossRef
  • What is Essential is (No More) Invisible to the Eyes: The Introduction of BlocDoc in the Digital Pathology Workflow
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    Journal of Pathology Informatics.2021; 12(1): 32.     CrossRef
Artificial Intelligence in Pathology
Hye Yoon Chang, Chan Kwon Jung, Junwoo Isaac Woo, Sanghun Lee, Joonyoung Cho, Sun Woo Kim, Tae-Yeong Kwak
J Pathol Transl Med. 2019;53(1):1-12.   Published online December 28, 2018
DOI: https://doi.org/10.4132/jptm.2018.12.16
  • 27,169 View
  • 1,248 Download
  • 117 Web of Science
  • 131 Crossref
AbstractAbstract PDF
As in other domains, artificial intelligence is becoming increasingly important in medicine. In particular,deep learning-based pattern recognition methods can advance the field of pathology byincorporating clinical, radiologic, and genomic data to accurately diagnose diseases and predictpatient prognoses. In this review, we present an overview of artificial intelligence, the brief historyof artificial intelligence in the medical domain, recent advances in artificial intelligence applied topathology, and future prospects of pathology driven by artificial intelligence.

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  • Exploring the status of artificial intelligence for healthcare research in Africa: a bibliometric and thematic analysis
    Tabu S. Kondo, Salim A. Diwani, Ally S. Nyamawe, Mohamed M. Mjahidi
    AI and Ethics.2025; 5(1): 117.     CrossRef
  • Artificial intelligence versus human analysis: Interpreting data in elderly fat reduction study
    Piotr Sporek, Mariusz Konieczny
    Advances in Integrative Medicine.2025; 12(1): 13.     CrossRef
  • Artificial intelligence in healthcare applications targeting cancer diagnosis—part I: data structure, preprocessing and data organization
    Anna Luíza Damaceno Araújo, Marcelo Sperandio, Giovanna Calabrese, Sarah S. Faria, Diego Armando Cardona Cardenas, Manoela Domingues Martins, Cristina Saldivia-Siracusa, Daniela Giraldo-Roldán, Caique Mariano Pedroso, Pablo Agustin Vargas, Marcio Ajudarte
    Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology.2025;[Epub]     CrossRef
  • Artificial intelligence–driven digital pathology in urological cancers: current trends and future directions
    Inyoung Paik, Geongyu Lee, Joonho Lee, Tae-Yeong Kwak, Hong Koo Ha
    Prostate International.2025;[Epub]     CrossRef
  • Optimizing deep learning for accurate blood cell classification: A study on stain normalization and fine-tuning techniques
    Mohammed Tareq Mutar, Jaffar Nouri Alalsaidissa, Mustafa Majid Hameed, Ali Almothaffar
    Iraqi Journal of Hematology.2025;[Epub]     CrossRef
  • Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives
    Ekta Jain, Ankush Patel, Anil V. Parwani, Saba Shafi, Zoya Brar, Shivani Sharma, Sambit K. Mohanty
    International Journal of Surgical Pathology.2024; 32(3): 433.     CrossRef
  • ChatGPT as an aid for pathological diagnosis of cancer
    Shaivy Malik, Sufian Zaheer
    Pathology - Research and Practice.2024; 253: 154989.     CrossRef
  • Computational pathology: A survey review and the way forward
    Mahdi S. Hosseini, Babak Ehteshami Bejnordi, Vincent Quoc-Huy Trinh, Lyndon Chan, Danial Hasan, Xingwen Li, Stephen Yang, Taehyo Kim, Haochen Zhang, Theodore Wu, Kajanan Chinniah, Sina Maghsoudlou, Ryan Zhang, Jiadai Zhu, Samir Khaki, Andrei Buin, Fatemeh
    Journal of Pathology Informatics.2024; 15: 100357.     CrossRef
  • Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis
    Nishath Sayed Abdul, Ganiga Channaiah Shivakumar, Sunila Bukanakere Sangappa, Marco Di Blasio, Salvatore Crimi, Marco Cicciù, Giuseppe Minervini
    BMC Oral Health.2024;[Epub]     CrossRef
  • Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center
    Ruey-Hsing Chou, Benny Wei-Yun Hsu, Chun-Lin Yu, Tai-Yuan Chen, Shuo-Ming Ou, Kuo-Hua Lee, Vincent S. Tseng, Po-Hsun Huang, Der-Cherng Tarng
    Journal of the Chinese Medical Association.2024; 87(4): 369.     CrossRef
  • The Evaluation of Artificial Intelligence Technology for the Differentiation of Fresh Human Blood Cells From Other Species Blood in the Investigation of Crime Scenes
    Syed Sajid Hussain Shah, Ekramy Elmorsy, Rashad Qasem Ali Othman, Asmara Syed, Syed Umar Armaghan, Syed Usama Khalid Bokhari, Mahmoud E Elmorsy, Abdulhakim Bawadekji
    Cureus.2024;[Epub]     CrossRef
  • A Comparison of Diagnostic and Immunohistochemical Workup and Literature Review Capabilities of Online Artificial Intelligence Assistance Models in Pathology
    Johnika Dougan, Netra Patel, Svetoslav Bardarov
    Cureus.2024;[Epub]     CrossRef
  • ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology–Head and Neck Surgery
    Cai Long, Deepak Subburam, Kayle Lowe, André dos Santos, Jessica Zhang, Sang Hwang, Neil Saduka, Yoav Horev, Tao Su, David W.J. Côté, Erin D. Wright
    Otolaryngology–Head and Neck Surgery.2024; 171(4): 1042.     CrossRef
  • Artificial intelligence in forensic medicine and related sciences – selected issues = Sztuczna inteligencja w medycynie sądowej i naukach pokrewnych – wybrane zagadnienia
    Michał Szeremeta, Julia Janica, Anna Niemcunowicz-Janica
    Archives of Forensic Medicine and Criminology.2024; 74(1): 64.     CrossRef
  • Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis
    Lei Yuan, Zhiming Shen, Yibo Shan, Jianwei Zhu, Qi Wang, Yi Lu, Hongcan Shi
    Frontiers in Oncology.2024;[Epub]     CrossRef
  • PathEX: Make good choice for whole slide image extraction
    Xinda Yang, Ranze Zhang, Yuan Yang, Yu Zhang, Kai Chen, Alberto Marchisio
    PLOS ONE.2024; 19(8): e0304702.     CrossRef
  • Automatic point detection on cephalograms using convolutional neural networks: A two-step method
    Miki HORI, Makoto JINCHO, Tadasuke HORI, Hironao SEKINE, Akiko KATO, Ken MIYAZAWA, Tatsushi KAWAI
    Dental Materials Journal.2024; 43(5): 701.     CrossRef
  • The use of generative artificial intelligence (AI) in teaching and assessment of postgraduate students in pathology and microbiology
    Dipmala Das, Asitava Deb Roy, Subhayan Dasgupta, Rohon Das Roy
    Indian Journal of Microbiology Research.2024; 11(3): 140.     CrossRef
  • Prioritize Threat Alerts Based on False Positives Qualifiers Provided by Multiple AI Models Using Evolutionary Computation and Reinforcement Learning
    Anup Sharma, V. G. Kiran Kumar, Asmita Poojari
    Journal of The Institution of Engineers (India): Series B.2024;[Epub]     CrossRef
  • Inteligencia artificial: desafíos éticos y futuros
    Jhadson Silva Leonel, Camila Ferreira Silva Leonel, Jonas Byk, Silvania da Conceição Furtado
    Revista Bioética.2024;[Epub]     CrossRef
  • Artificial intelligence: ethical and future challenges
    Jhadson Silva Leonel, Camila Ferreira Silva Leonel, Jonas Byk, Silvania da Conceição Furtado
    Revista Bioética.2024;[Epub]     CrossRef
  • Inteligência artificial: desafios éticos e futuros
    Jhadson Silva Leonel, Camila Ferreira Silva Leonel, Jonas Byk, Silvania da Conceição Furtado
    Revista Bioética.2024;[Epub]     CrossRef
  • The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems
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    Clinics and Practice.2023; 13(4): 994.     CrossRef
  • Building a nonclinical pathology laboratory of the future for pharmaceutical research excellence
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    Drug Discovery Today.2023; 28(10): 103747.     CrossRef
  • Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis
    Nelli Sjöblom, Sonja Boyd, Anniina Manninen, Sami Blom, Anna Knuuttila, Martti Färkkilä, Johanna Arola
    Hepatology Research.2023; 53(4): 322.     CrossRef
  • Application of convolutional neural network for analyzing hepatic fibrosis in mice
    Hyun-Ji Kim, Eun Bok Baek, Ji-Hee Hwang, Minyoung Lim, Won Hoon Jung, Myung Ae Bae, Hwa-Young Son, Jae-Woo Cho
    Journal of Toxicologic Pathology.2023; 36(1): 21.     CrossRef
  • Machine Learning Techniques for Prognosis Estimation and Knowledge Discovery From Lab Test Results With Application to the COVID-19 Emergency
    Alfonso Emilio Gerevini, Roberto Maroldi, Matteo Olivato, Luca Putelli, Ivan Serina
    IEEE Access.2023; 11: 83905.     CrossRef
  • Artificial intelligence in dentistry—A review
    Hao Ding, Jiamin Wu, Wuyuan Zhao, Jukka P. Matinlinna, Michael F. Burrow, James K. H. Tsoi
    Frontiers in Dental Medicine.2023;[Epub]     CrossRef
  • Dental Age Estimation Using the Demirjian Method: Statistical Analysis Using Neural Networks
    Byung-Yoon Roh, Jong-Seok Lee, Sang-Beom Lim, Hye-Won Ryu, Su-Jeong Jeon, Ju-Heon Lee, Yo-Seob Seo, Ji-Won Ryu, Jong-Mo Ahn
    Korean Journal of Legal Medicine.2023; 47(1): 1.     CrossRef
  • The use of artificial intelligence in health care. Problems of identification of patients' conditions in the processes of detailing the diagnosis
    Mintser O
    Artificial Intelligence.2023; 28(AI.2023.28): 8.     CrossRef
  • The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —
    Hiroyuki NOZAKA, Kosuke KAMATA, Kazufumi YAMAGATA
    IEICE Transactions on Information and Systems.2023; E106.D(5): 707.     CrossRef
  • Rectal Cancer Stages T2 and T3 Identification Based on Asymptotic Hybrid Feature Maps
    Shujing Sun, Jiale Wu, Jian Yao, Yang Cheng, Xin Zhang, Zhihua Lu, Pengjiang Qian
    Computer Modeling in Engineering & Sciences.2023; 137(1): 923.     CrossRef
  • How to use AI in pathology
    Peter Schüffler, Katja Steiger, Wilko Weichert
    Genes, Chromosomes and Cancer.2023; 62(9): 564.     CrossRef
  • Cutting-Edge Technologies for Digital Therapeutics: A Review and Architecture Proposals for Future Directions
    Joo Hun Yoo, Harim Jeong, Tai-Myoung Chung
    Applied Sciences.2023; 13(12): 6929.     CrossRef
  • A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer
    Connor Stashko, Mary-Kate Hayward, Jason J. Northey, Neil Pearson, Alastair J. Ironside, Johnathon N. Lakins, Roger Oria, Marie-Anne Goyette, Lakyn Mayo, Hege G. Russnes, E. Shelley Hwang, Matthew L. Kutys, Kornelia Polyak, Valerie M. Weaver
    Nature Communications.2023;[Epub]     CrossRef
  • Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study
    Palak Patel, Stephanie Harmon, Rachael Iseman, Olga Ludkowski, Heidi Auman, Sarah Hawley, Lisa F. Newcomb, Daniel W. Lin, Peter S. Nelson, Ziding Feng, Hilary D. Boyer, Maria S. Tretiakova, Larry D. True, Funda Vakar-Lopez, Peter R. Carroll, Matthew R. Co
    Modern Pathology.2023; 36(10): 100241.     CrossRef
  • Minimum resolution requirements of digital pathology images for accurate classification
    Lydia Neary-Zajiczek, Linas Beresna, Benjamin Razavi, Vijay Pawar, Michael Shaw, Danail Stoyanov
    Medical Image Analysis.2023; 89: 102891.     CrossRef
  • Artificial Intelligence in the Pathology of Gastric Cancer
    Sangjoon Choi, Seokhwi Kim
    Journal of Gastric Cancer.2023; 23(3): 410.     CrossRef
  • Endoscopic Ultrasound-Based Artificial Intelligence Diagnosis of Pancreatic Cystic Neoplasms
    Jin-Seok Park, Seok Jeong
    The Korean Journal of Pancreas and Biliary Tract.2023; 28(3): 53.     CrossRef
  • Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine
    Thomas Gniadek, Jason Kang, Talent Theparee, Jacob Krive
    Online Journal of Public Health Informatics.2023; 15: e50934.     CrossRef
  • A Literature Review of the Future of Oral Medicine and Radiology, Oral Pathology, and Oral Surgery in the Hands of Technology
    Ishita Singhal, Geetpriya Kaur, Dirk Neefs, Aparna Pathak
    Cureus.2023;[Epub]     CrossRef
  • AI-Powered Biomolecular-Specific and Label-Free Multispectral Imaging Rapidly Detects Malignant Neoplasm in Surgically Excised Breast Tissue Specimens
    Rishikesh Pandey, David Fournier, Gary Root, Machele Riccio, Aditya Shirvalkar, Gianfranco Zamora, Noel Daigneault, Michael Sapack, Minghao Zhong, Malini Harigopal
    Archives of Pathology & Laboratory Medicine.2023; 147(11): 1298.     CrossRef
  • Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review
    Dacre R.T. Knight, Christopher A. Aakre, Christopher V. Anstine, Bala Munipalli, Parisa Biazar, Ghada Mitri, Jose Raul Valery, Tara Brigham, Shehzad K. Niazi, Adam I. Perlman, John D. Halamka, Abd Moain Abu Dabrh
    Health Policy and Technology.2023; 12(4): 100824.     CrossRef
  • Towards Autonomous Healthcare: Integrating Artificial Intelligence (AI) for Personalized Medicine and Disease Prediction
    Nitin Rane, Saurabh Choudhary, Jayesh Rane
    SSRN Electronic Journal.2023;[Epub]     CrossRef
  • Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges
    Kevin Pierre, Manas Gupta, Abheek Raviprasad, Seyedeh Mehrsa Sadat Razavi, Anjali Patel, Keith Peters, Bruno Hochhegger, Anthony Mancuso, Reza Forghani
    Expert Review of Anticancer Therapy.2023; 23(12): 1265.     CrossRef
  • Automated differential diagnostics of respiratory diseases using an electronic stethoscope
    Diana Arhypenko, Denis Panaskin, Dmytro Babko
    Polish Journal of Medical Physics and Engineering.2023; 29(4): 208.     CrossRef
  • Application of machine learning in identification of pathogenic microbes
    Lakshmi Venkata S Kutikuppala, Kanishk K Adhit, Reewen George D Silva
    Digital Medicine.2023;[Epub]     CrossRef
  • The Beginning of a New Era
    C Nandini, Shaik Basha, Aarchi Agarawal, R Parikh Neelampari, Krishna P Miyapuram, R Jadeja Nileshwariba
    Advances in Human Biology.2023; 13(1): 4.     CrossRef
  • Artificial Intelligence in Respiratory Medicine
    K Kalaiyarasan, R Sridhar
    Journal of Association of Pulmonologist of Tamil Nadu.2023; 6(2): 53.     CrossRef
  • Automated abstraction of myocardial perfusion imaging reports using natural language processing
    Parija Sharedalal, Ajay Singh, Neal Shah, Diwakar Jain
    Journal of Nuclear Cardiology.2022; 29(3): 1188.     CrossRef
  • Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer
    Geetanjali Saini, Shriya Joshi, Chakravarthy Garlapati, Hongxiao Li, Jun Kong, Jayashree Krishnamurthy, Michelle D. Reid, Ritu Aneja
    Seminars in Cancer Biology.2022; 81: 220.     CrossRef
  • A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
    Yixin Li, Chen Li, Xiaoyan Li, Kai Wang, Md Mamunur Rahaman, Changhao Sun, Hao Chen, Xinran Wu, Hong Zhang, Qian Wang
    Archives of Computational Methods in Engineering.2022; 29(1): 609.     CrossRef
  • Artificial intelligence in oncology: From bench to clinic
    Jamal Elkhader, Olivier Elemento
    Seminars in Cancer Biology.2022; 84: 113.     CrossRef
  • Yeast‐like organisms phagocytosed by circulating neutrophils: Evidence of disseminated histoplasmosis
    Yue Zhao, Jenna McCracken, Endi Wang
    International Journal of Laboratory Hematology.2022; 44(1): 51.     CrossRef
  • Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review
    Aleksandra Zuraw, Famke Aeffner
    Veterinary Pathology.2022; 59(1): 6.     CrossRef
  • A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
    Xintong Li, Chen Li, Md Mamunur Rahaman, Hongzan Sun, Xiaoqi Li, Jian Wu, Yudong Yao, Marcin Grzegorzek
    Artificial Intelligence Review.2022; 55(6): 4809.     CrossRef
  • Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
    Octav Ginghina, Ariana Hudita, Marius Zamfir, Andrada Spanu, Mara Mardare, Irina Bondoc, Laura Buburuzan, Sergiu Emil Georgescu, Marieta Costache, Carolina Negrei, Cornelia Nitipir, Bianca Galateanu
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Automated bone marrow cytology using deep learning to generate a histogram of cell types
    Rohollah Moosavi Tayebi, Youqing Mu, Taher Dehkharghanian, Catherine Ross, Monalisa Sur, Ronan Foley, Hamid R. Tizhoosh, Clinton J. V. Campbell
    Communications Medicine.2022;[Epub]     CrossRef
  • Risultati di esami di laboratorio per intelligenza artificiale e "machine learning"
    Marco PRADELLA
    La Rivista Italiana della Medicina di Laboratorio.2022;[Epub]     CrossRef
  • The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach
    Florian Funer
    Medicine, Health Care and Philosophy.2022; 25(2): 167.     CrossRef
  • Deep discriminative learning model with calibrated attention map for the automated diagnosis of diffuse large B-cell lymphoma
    Sautami Basu, Ravinder Agarwal, Vishal Srivastava
    Biomedical Signal Processing and Control.2022; 76: 103728.     CrossRef
  • Question and Answer Techniques for Financial Audits in Universities Based on Deep Learning
    Qiang Li, Hangjun Che
    Mathematical Problems in Engineering.2022; 2022: 1.     CrossRef
  • Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study
    Erdenebayar Urtnasan, Jung Hun Lee, Byungjin Moon, Hee Young Lee, Kyuhee Lee, Hyun Youk
    JMIR Medical Informatics.2022; 10(6): e34724.     CrossRef
  • Impact of artificial intelligence on pathologists’ decisions: an experiment
    Julien Meyer, April Khademi, Bernard Têtu, Wencui Han, Pria Nippak, David Remisch
    Journal of the American Medical Informatics Association.2022; 29(10): 1688.     CrossRef
  • Rapid Screening Using Pathomorphologic Interpretation to Detect BRAFV600E Mutation and Microsatellite Instability in Colorectal Cancer
    Satoshi Fujii, Daisuke Kotani, Masahiro Hattori, Masato Nishihara, Toshihide Shikanai, Junji Hashimoto, Yuki Hama, Takuya Nishino, Mizuto Suzuki, Ayatoshi Yoshidumi, Makoto Ueno, Yoshito Komatsu, Toshiki Masuishi, Hiroki Hara, Taito Esaki, Yoshiaki Nakamu
    Clinical Cancer Research.2022; 28(12): 2623.     CrossRef
  • Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry
    Sean A. Rasmussen, Valerie J. Taylor, Alexi P. Surette, Penny J. Barnes, Gillian C. Bethune
    Applied Immunohistochemistry & Molecular Morphology.2022; 30(10): 668.     CrossRef
  • Deep Neural Network for the Prediction of KRAS Genotype in Rectal Cancer
    Waleed M Ghareeb, Eman Draz, Khaled Madbouly, Ahmed H Hussein, Mohammed Faisal, Wagdi Elkashef, Mona Hany Emile, Marcus Edelhamre, Seon Hahn Kim, Sameh Hany Emile
    Journal of the American College of Surgeons.2022; 235(3): 482.     CrossRef
  • Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology
    Alex Dexter, Dimitrios Tsikritsis, Natalie A. Belsey, Spencer A. Thomas, Jenny Venton, Josephine Bunch, Marina Romanchikova
    Journal of Molecular Pathology.2022; 3(3): 168.     CrossRef
  • Animation Design of Multisensor Data Fusion Based on Optimized AVOD Algorithm
    Li Ding, Guobing Wei, Kai Zhang, Gengxin Sun
    Journal of Sensors.2022; 2022: 1.     CrossRef
  • Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing
    Yan Gong
    ACM Transactions on Asian and Low-Resource Language Information Processing.2022;[Epub]     CrossRef
  • Analysis and Estimation of Pathological Data and Findings with Deep Learning Methods
    Ahmet Anıl ŞAKIR, Ali Hakan IŞIK, Özlem ÖZMEN, Volkan İPEK
    Veterinary Journal of Mehmet Akif Ersoy University.2022; 7(3): 175.     CrossRef
  • Artificial Intelligence in Pathology: Friend or Enemy?
    Selim Sevim, Ezgi Dicle Serbes, Murat Bahadır, Mustafa Said Kartal, Serpil Dizbay Sak
    Journal of Ankara University Faculty of Medicine.2022; 75(1): 13.     CrossRef
  • Assessment of knowledge, attitude, and practice regarding artificial intelligence in histopathology
    M. Indu, Vidya Gurram Shankar, Latha Mary Cherian, Revathi Krishna, Sabu Paul, Pradeesh Sathyan
    Saudi Journal of Oral Sciences.2022; 9(3): 157.     CrossRef
  • Evaluation Challenges in the Validation of B7-H3 as Oral Tongue Cancer Prognosticator
    Meri Sieviläinen, Anna Maria Wirsing, Aini Hyytiäinen, Rabeia Almahmoudi, Priscila Rodrigues, Inger-Heidi Bjerkli, Pirjo Åström, Sanna Toppila-Salmi, Timo Paavonen, Ricardo D. Coletta, Elin Hadler-Olsen, Tuula Salo, Ahmed Al-Samadi
    Head and Neck Pathology.2021; 15(2): 469.     CrossRef
  • Amsterdam International Consensus Meeting: tumor response scoring in the pathology assessment of resected pancreatic cancer after neoadjuvant therapy
    Boris V. Janssen, Faik Tutucu, Stijn van Roessel, Volkan Adsay, Olca Basturk, Fiona Campbell, Claudio Doglioni, Irene Esposito, Roger Feakins, Noriyoshi Fukushima, Anthony J. Gill, Ralph H. Hruban, Jeffrey Kaplan, Bas Groot Koerkamp, Seung-Mo Hong, Alyssa
    Modern Pathology.2021; 34(1): 4.     CrossRef
  • Fabrication of ultra-thin 2D covalent organic framework nanosheets and their application in functional electronic devices
    Weikang Wang, Weiwei Zhao, Haotian Xu, Shujuan Liu, Wei Huang, Qiang Zhao
    Coordination Chemistry Reviews.2021; 429: 213616.     CrossRef
  • Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
    Hyun-Jong Jang, In Hye Song, Sung Hak Lee
    Applied Sciences.2021; 11(2): 808.     CrossRef
  • Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center
    Peter J Schüffler, Luke Geneslaw, D Vijay K Yarlagadda, Matthew G Hanna, Jennifer Samboy, Evangelos Stamelos, Chad Vanderbilt, John Philip, Marc-Henri Jean, Lorraine Corsale, Allyne Manzo, Neeraj H G Paramasivam, John S Ziegler, Jianjiong Gao, Juan C Peri
    Journal of the American Medical Informatics Association.2021; 28(9): 1874.     CrossRef
  • Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis
    Julia Moran-Sanchez, Antonio Santisteban-Espejo, Miguel Angel Martin-Piedra, Jose Perez-Requena, Marcial Garcia-Rojo
    Biomolecules.2021; 11(6): 793.     CrossRef
  • Development and operation of a digital platform for sharing pathology image data
    Yunsook Kang, Yoo Jung Kim, Seongkeun Park, Gun Ro, Choyeon Hong, Hyungjoon Jang, Sungduk Cho, Won Jae Hong, Dong Un Kang, Jonghoon Chun, Kyoungbun Lee, Gyeong Hoon Kang, Kyoung Chul Moon, Gheeyoung Choe, Kyu Sang Lee, Jeong Hwan Park, Won-Ki Jeong, Se Yo
    BMC Medical Informatics and Decision Making.2021;[Epub]     CrossRef
  • Sliding window based deep ensemble system for breast cancer classification
    Amin Alqudah, Ali Mohammad Alqudah
    Journal of Medical Engineering & Technology.2021; 45(4): 313.     CrossRef
  • Artificial intelligence and computational pathology
    Miao Cui, David Y. Zhang
    Laboratory Investigation.2021; 101(4): 412.     CrossRef
  • Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models
    Elham Vali-Betts, Kevin J. Krause, Alanna Dubrovsky, Kristin Olson, John Paul Graff, Anupam Mitra, Ananya Datta-Mitra, Kenneth Beck, Aristotelis Tsirigos, Cynthia Loomis, Antonio Galvao Neto, Esther Adler, Hooman H. Rashidi
    Journal of Pathology Informatics.2021; 12(1): 5.     CrossRef
  • Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancer
    Sung Hak Lee, In Hye Song, Hyun‐Jong Jang
    International Journal of Cancer.2021; 149(3): 728.     CrossRef
  • Artificial intelligence in healthcare
    Yamini D Shah, Shailvi M Soni, Manish P Patel
    Indian Journal of Pharmacy and Pharmacology.2021; 8(2): 102.     CrossRef
  • Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis
    Agathe Bédard, Thomas Westerling-Bui, Aleksandra Zuraw
    Toxicologic Pathology.2021; 49(4): 897.     CrossRef
  • An empirical analysis of machine learning frameworks for digital pathology in medical science
    S.K.B. Sangeetha, R Dhaya, Dhruv T Shah, R Dharanidharan, K. Praneeth Sai Reddy
    Journal of Physics: Conference Series.2021; 1767(1): 012031.     CrossRef
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    Daniel Royston, Adam J. Mead, Bethan Psaila
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    Rolf Teschke, Gaby Danan
    Diagnostics.2021; 11(3): 458.     CrossRef
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    Hamid R. Tizhoosh, Phedias Diamandis, Clinton J.V. Campbell, Amir Safarpoor, Shivam Kalra, Danial Maleki, Abtin Riasatian, Morteza Babaie
    The American Journal of Pathology.2021; 191(10): 1702.     CrossRef
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    Yao Yao, Shuiping Gou, Ru Tian, Xiangrong Zhang, Shuixiang He, Zhiguo Zhou
    BioMed Research International.2021;[Epub]     CrossRef
  • Artificial intelligence and sleep: Advancing sleep medicine
    Nathaniel F. Watson, Christopher R. Fernandez
    Sleep Medicine Reviews.2021; 59: 101512.     CrossRef
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    Journal of Medical Internet Research.2021; 23(2): e24221.     CrossRef
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    Bioinformatics.2021; 37(22): 4291.     CrossRef
  • A Review of Applications of Artificial Intelligence in Gastroenterology
    Khalid Nawab, Ravi Athwani, Awais Naeem, Muhammad Hamayun, Momna Wazir
    Cureus.2021;[Epub]     CrossRef
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    Frontiers in Oncology.2021;[Epub]     CrossRef
  • Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
    Hyeongsub Kim, Hongjoon Yoon, Nishant Thakur, Gyoyeon Hwang, Eun Jung Lee, Chulhong Kim, Yosep Chong
    Scientific Reports.2021;[Epub]     CrossRef
  • Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study
    Veronika Shavlokhova, Sameena Sandhu, Christa Flechtenmacher, Istvan Koveshazi, Florian Neumeier, Víctor Padrón-Laso, Žan Jonke, Babak Saravi, Michael Vollmer, Andreas Vollmer, Jürgen Hoffmann, Michael Engel, Oliver Ristow, Christian Freudlsperger
    Journal of Clinical Medicine.2021; 10(22): 5326.     CrossRef
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    Journal of Pathology Informatics.2021; 12(1): 45.     CrossRef
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    Sotirios Bisdas, Constantin-Cristian Topriceanu, Zosia Zakrzewska, Alexandra-Valentina Irimia, Loizos Shakallis, Jithu Subhash, Maria-Madalina Casapu, Jose Leon-Rojas, Daniel Pinto dos Santos, Dilys Miriam Andrews, Claudia Zeicu, Ahmad Mohammad Bouhuwaish
    Frontiers in Public Health.2021;[Epub]     CrossRef
  • Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review
    Robert Y. Osamura, Naruaki Matsui, Masato Kawashima, Hiroyasu Saiga, Maki Ogura, Tomoharu Kiyuna
    Acta Cytologica.2021; 65(4): 342.     CrossRef
  • Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging
    Frederik Großerueschkamp, Hendrik Jütte, Klaus Gerwert, Andrea Tannapfel
    Visceral Medicine.2021; 37(6): 482.     CrossRef
  • Feasibility of fully automated classification of whole slide images based on deep learning
    Kyung-Ok Cho, Sung Hak Lee, Hyun-Jong Jang
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    Joshua Kubach, Angelika Muhlebner‐Fahrngruber, Figen Soylemezoglu, Hajime Miyata, Pitt Niehusmann, Mrinalini Honavar, Fabio Rogerio, Se‐Hoon Kim, Eleonora Aronica, Rita Garbelli, Samuel Vilz, Alexander Popp, Stefan Walcher, Christoph Neuner, Michael Schol
    Epilepsia.2020; 61(3): 421.     CrossRef
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    Tahsin Kurc, Spyridon Bakas, Xuhua Ren, Aditya Bagari, Alexandre Momeni, Yue Huang, Lichi Zhang, Ashish Kumar, Marc Thibault, Qi Qi, Qian Wang, Avinash Kori, Olivier Gevaert, Yunlong Zhang, Dinggang Shen, Mahendra Khened, Xinghao Ding, Ganapathy Krishnamu
    Frontiers in Neuroscience.2020;[Epub]     CrossRef
  • Artificial intelligence as the next step towards precision pathology
    B. Acs, M. Rantalainen, J. Hartman
    Journal of Internal Medicine.2020; 288(1): 62.     CrossRef
  • Introduction to digital pathology and computer-aided pathology
    Soojeong Nam, Yosep Chong, Chan Kwon Jung, Tae-Yeong Kwak, Ji Youl Lee, Jihwan Park, Mi Jung Rho, Heounjeong Go
    Journal of Pathology and Translational Medicine.2020; 54(2): 125.     CrossRef
  • Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
    Zeeshan Ahmed, Khalid Mohamed, Saman Zeeshan, XinQi Dong
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    Abigail R. Trachtman, Luca Bergamini, Andrea Palazzi, Angelo Porrello, Andrea Capobianco Dondona, Ercole Del Negro, Andrea Paolini, Giorgio Vignola, Simone Calderara, Giuseppe Marruchella
    Veterinary Research.2020;[Epub]     CrossRef
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    Nina Schwalbe, Brian Wahl
    The Lancet.2020; 395(10236): 1579.     CrossRef
  • The future of pathology is digital
    J.D. Pallua, A. Brunner, B. Zelger, M. Schirmer, J. Haybaeck
    Pathology - Research and Practice.2020; 216(9): 153040.     CrossRef
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    Scientific Reports.2020;[Epub]     CrossRef
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    Anil V. Parwani, Mahul B. Amin
    Advances in Anatomic Pathology.2020; 27(4): 221.     CrossRef
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    World Journal of Gastroenterology.2020; 26(40): 6207.     CrossRef
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    M P Diakovich, M V Krivov
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    Zhongmin Li, Silvia Goebel, Andreas Reimann, Martin Ungerer
    Scientific Reports.2020;[Epub]     CrossRef
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    SSRN Electronic Journal .2019;[Epub]     CrossRef
Original Article
Difference of the Nuclear Green Light Intensity between Papillary Carcinoma Cells Showing Clear Nuclei and Non-neoplastic Follicular Epithelia in Papillary Thyroid Carcinoma
Hyekyung Lee, Tae Hwa Baek, Meeja Park, Seung Yun Lee, Hyun Jin Son, Dong Wook Kang, Joo Heon Kim, Soo Young Kim
J Pathol Transl Med. 2016;50(5):355-360.   Published online August 22, 2016
DOI: https://doi.org/10.4132/jptm.2016.05.19
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AbstractAbstract PDF
Background
There is subjective disagreement regarding nuclear clearing in papillary thyroid carcinoma. In this study, using digital instruments, we were able to quantify many ambiguous pathologic features and use numeric data to express our findings.
Methods
We examined 30 papillary thyroid carcinomas. For each case, we selected representative cancer cells showing clear nuclei and surrounding non-neoplastic follicular epithelial cells and evaluated objective values of green light intensity (GLI) for quantitative analysis of nuclear clearing in papillary thyroid carcinoma.
Results
From 16,274 GLI values from 600 cancer cell nuclei and 13,752 GLI values from 596 non-neoplastic follicular epithelial nuclei, we found a high correlation of 94.9% between GLI and clear nuclei. GLI between the cancer group showing clear nuclei and non-neoplastic follicular epithelia was statistically significant. The overall average level of GLI in the cancer group was over two times higher than the non-neoplastic group despite a wide range of GLI. On a polygonal line graph, there was a fluctuating unique difference between both the cancer and non-neoplastic groups in each patient, which was comparable to the microscopic findings.
Conclusions
Nuclear GLI could be a useful factor for discriminating between carcinoma cells showing clear nuclei and non-neoplastic follicular epithelia in papillary thyroid carcinoma.
Reviews
Pathology Reporting of Thyroid Core Needle Biopsy: A Proposal of the Korean Endocrine Pathology Thyroid Core Needle Biopsy Study Group
Chan Kwon Jung, Hye Sook Min, Hyo Jin Park, Dong Eun Song, Jang Hee Kim, So Yeon Park, Hyunju Yoo, Mi Kyung Shin, Korean Endocrine Pathology Thyroid Core Needle Biopsy Study Group
J Pathol Transl Med. 2015;49(4):288-299.   Published online June 17, 2015
DOI: https://doi.org/10.4132/jptm.2015.06.04
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AbstractAbstract PDF
In recent years throughout Korea, the use of ultrasound-guided core needle biopsy (CNB) has become common for the preoperative diagnosis of thyroid nodules. However, there is no consensus on the pathology reporting system for thyroid CNB. The Korean Endocrine Pathology Thyroid Core Needle Biopsy Study Group held a conference on thyroid CNB pathology and developed guidelines through contributions from the participants. This article discusses the outcome of the discussions that led to a consensus on the pathology reporting of thyroid CNB.

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  • The Diagnostic Role of Repeated Biopsy of Thyroid Nodules with Atypia of Undetermined Significance with Architectural Atypia on Core-Needle Biopsy
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    Endocrinology and Metabolism.2024; 39(2): 300.     CrossRef
  • Core needle biopsy for thyroid nodules assessment-a new horizon?
    David D Dolidze, Serghei Covantsev, Grigorii M Chechenin, Natalia V Pichugina, Anastasia V Bedina, Anna Bumbu
    World Journal of Clinical Oncology.2024; 15(5): 580.     CrossRef
  • Ultrasonographic features and diagnostic accuracy of FNA and CNB in secondary thyroid malignancies: A retrospective study
    Zhen Xia, Xiaochen Huang, Ting Zhang, Zhigang Gao, Xiuliang Tang, Wei Zhang, Qing Miao
    Heliyon.2024; 10(16): e36305.     CrossRef
  • Current role of interventional radiology in thyroid nodules
    Onur Taydas, Erbil Arik, Omer Faruk Sevinc, Ahmet Burak Kara, Mustafa Ozdemir, Hasret Cengiz, Zulfu Bayhan, Mehmet Halil Ozturk
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
  • A simplified four-tier classification for thyroid core needle biopsy
    M. Paja, J. L. Del Cura, R. Zabala, I. Korta, Mª T. Gutiérrez, A. Expósito, A. Ugalde
    Journal of Endocrinological Investigation.2024; 48(4): 895.     CrossRef
  • Follow-up of benign thyroid nodules confirmed by ultrasound-guided core needle biopsy after inconclusive cytology on fine-needle aspiration biopsy
    Yoon Ji Hwang, Hye Ryoung Koo, Jeong Seon Park
    Ultrasonography.2023; 42(1): 121.     CrossRef
  • Subcategorization of intermediate suspicion thyroid nodules based on suspicious ultrasonographic findings
    Haejung Kim, Jung Hee Shin, Ka Eun Kim, Myoung Kyoung Kim, Jiyun Oh, Soo Yeon Hahn
    Ultrasonography.2023; 42(2): 307.     CrossRef
  • Preoperative Risk Stratification of Follicular-patterned Thyroid Lesions on Core Needle Biopsy by Histologic Subtyping and RAS Variant-specific Immunohistochemistry
    Meejeong Kim, Sora Jeon, Chan Kwon Jung
    Endocrine Pathology.2023; 34(2): 247.     CrossRef
  • Cytological and Ultrasound Features of Thyroid Nodules Correlate With Histotypes and Variants of Thyroid Carcinoma
    Daniele Sgrò, Alessandro Brancatella, Giuseppe Greco, Liborio Torregrossa, Paolo Piaggi, Nicola Viola, Teresa Rago, Fulvio Basolo, Riccardo Giannini, Gabriele Materazzi, Rossella Elisei, Ferruccio Santini, Francesco Latrofa
    The Journal of Clinical Endocrinology & Metabolism.2023; 108(11): e1186.     CrossRef
  • Reevaluating diagnostic categories and associated malignancy risks in thyroid core needle biopsy
    Chan Kwon Jung
    Journal of Pathology and Translational Medicine.2023; 57(4): 208.     CrossRef
  • Contrast Enhancement Ultrasound Improves Diagnostic Accuracy for Thyroid Nodules: A Prospective Multicenter Study
    Jianming Li, Jianping Dou, Huarong Li, Fan Xiao, Jie Yu, Mingxing Xie, Ping Zhou, Lei Liang, Guiming Zhou, Ying Che, Cun Liu, Zhibin Cong, Fangyi Liu, Zhiyu Han, Ping Liang
    Journal of the Endocrine Society.2023;[Epub]     CrossRef
  • Core needle biopsy and ultrasonography are superior to fine needle aspiration in the management of follicular variant papillary thyroid carcinomas
    Ji-Ye Kim, Sunhee Chang, Ah-Young Kwon, Eun Young Park, Tae Hyuk Kim, Sangjoon Choi, Minju Lee, Young Lyun Oh
    Endocrine.2022; 75(2): 437.     CrossRef
  • Fine Needle Aspiration Cytology vs. Core Needle Biopsy for Thyroid Nodules: A Prospective, Experimental Study Using Surgical Specimen
    Hyuk Kwon, Jandee Lee, Soon Won Hong, Hyeong Ju Kwon, Jin Young Kwak, Jung Hyun Yoon
    Journal of the Korean Society of Radiology.2022; 83(3): 645.     CrossRef
  • Diagnostic efficacy, performance and safety of side-cut core needle biopsy for thyroid nodules: comparison of automated and semi-automated biopsy needles
    Ji Yeon Park, Seong Yoon Yi, Soo Heui Baek, Yu Hyun Lee, Heon-Ju Kwon, Hee Jin Park
    Endocrine.2022; 76(2): 341.     CrossRef
  • Approach to Bethesda system category III thyroid nodules according to US-risk stratification
    Jieun Kim, Jung Hee Shin, Young Lyun Oh, Soo Yeon Hahn, Ko Woon Park
    Endocrine Journal.2022; 69(1): 67.     CrossRef
  • Quantitative analysis of vascularity for thyroid nodules on ultrasound using superb microvascular imaging
    Min Ji Hong, Hye Shin Ahn, Su Min Ha, Hyun Jeong Park, Jiyun Oh
    Medicine.2022; 101(5): e28725.     CrossRef
  • Diagnostic Performance of Thyroid Core Needle Biopsy Using the Revised Reporting System: Comparison with Fine Needle Aspiration Cytology
    Kwangsoon Kim, Ja Seong Bae, Jeong Soo Kim, So Lyung Jung, Chan Kwon Jung
    Endocrinology and Metabolism.2022; 37(1): 159.     CrossRef
  • A Matched-Pair Analysis of Nuclear Morphologic Features Between Core Needle Biopsy and Surgical Specimen in Thyroid Tumors Using a Deep Learning Model
    Faridul Haq, Andrey Bychkov, Chan Kwon Jung
    Endocrine Pathology.2022; 33(4): 472.     CrossRef
  • Role of echogenic foci in ultrasonographic risk stratification of thyroid nodules: Echogenic focus scoring in the American College of Radiology Thyroid Imaging Reporting and Data System
    Renxu Li, Zhenwei Liang, Xiangyu Wang, Luzeng Chen
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Ultrasonographic characteristics of medullary thyroid carcinoma according to nodule size: application of the Korean Thyroid Imaging Reporting and Data System and American Thyroid Association guidelines
    Soo Yeon Hahn, Jung Hee Shin, Young Lyun Oh, Ko Woon Park
    Acta Radiologica.2021; 62(4): 474.     CrossRef
  • Malignancy risk of thyroid nodules with nonshadowing echogenic foci
    Yu-Mee Sohn, Dong Gyu Na, Wooyul Paik, Hye Yun Gwon, Byeong-Joo Noh
    Ultrasonography.2021; 40(1): 115.     CrossRef
  • Assessing the diagnostic performance of thyroid biopsy with recommendations for appropriate interpretation
    Su Min Ha, Jung Hwan Baek, Dong Gyu Na, Chan-Kwon Jung, Chong Hyun Suh, Young Kee Shong, Tae Yon Sung, Dong Eun Song, Jeong Hyun Lee
    Ultrasonography.2021; 40(2): 228.     CrossRef
  • Usage and Diagnostic Yield of Fine-Needle Aspiration Cytology and Core Needle Biopsy in Thyroid Nodules: A Systematic Review and Meta-Analysis of Literature Published by Korean Authors
    Soon-Hyun Ahn
    Clinical and Experimental Otorhinolaryngology.2021; 14(1): 116.     CrossRef
  • Comparison of the Efficacy and Safety of the American Thyroid Association Guidelines and American College of Radiology TI-RADS
    Jinghua Liu, Yajun Guo, Jiangxi Xiao, Luzeng Chen, Zhenwei Liang
    Endocrine Practice.2021; 27(7): 661.     CrossRef
  • Malignancy Rate of Bethesda Class III Thyroid Nodules Based on the Presence of Chronic Lymphocytic Thyroiditis in Surgical Patients
    Yoon Young Cho, Yun Jae Chung, Hee Sung Kim
    Frontiers in Endocrinology.2021;[Epub]     CrossRef
  • Efficacy of Differential Diagnosis of Thyroid Nodules by Shear Wave Elastography—the Stiffness Map
    Myung Hi Yoo, Hye Jeong Kim, In Ho Choi, Suyeon Park, Sumi Yun, Hyeong Kyu Park, Dong Won Byun, Kyoil Suh
    Journal of the Endocrine Society.2021;[Epub]     CrossRef
  • The relationship of thyroid nodule size on malignancy risk according to histological type of thyroid cancer
    Sae Rom Chung, Jung Hwan Baek, Young Jun Choi, Tae-Yon Sung, Dong Eun Song, Tae Yong Kim, Jeong Hyun Lee
    Acta Radiologica.2020; 61(5): 620.     CrossRef
  • Concordance of Three International Guidelines for Thyroid Nodules Classified by Ultrasonography and Diagnostic Performance of Biopsy Criteria
    Younghee Yim, Dong Gyu Na, Eun Ju Ha, Jung Hwan Baek, Jin Yong Sung, Ji-hoon Kim, Won-Jin Moon
    Korean Journal of Radiology.2020; 21(1): 108.     CrossRef
  • 2019 Practice guidelines for thyroid core needle biopsy: a report of the Clinical Practice Guidelines Development Committee of the Korean Thyroid Association
    Chan Kwon Jung, Jung Hwan Baek, Dong Gyu Na, Young Lyun Oh, Ka Hee Yi, Ho-Cheol Kang
    Journal of Pathology and Translational Medicine.2020; 54(1): 64.     CrossRef
  • Core-Needle Biopsy Does Not Show Superior Diagnostic Performance to Fine-Needle Aspiration for Diagnosing Thyroid Nodules
    Ilah Shin, Eun-Kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Vivian Youngjean Park, Si Eun Lee, Hye Sun Lee, Jin Young Kwak
    Yonsei Medical Journal.2020; 61(2): 161.     CrossRef
  • Ultrasound‐guided fine‐needle aspiration or core needle biopsy for diagnosing follicular thyroid carcinoma?
    Ko Woon Park, Jung Hee Shin, Soo Yeon Hahn, Young Lyun Oh, Sun Wook Kim, Tae Hyuk Kim, Jae Hoon Chung
    Clinical Endocrinology.2020; 92(5): 468.     CrossRef
  • Distribution and malignancy risk of six categories of the pathology reporting system for thyroid core-needle biopsy in 1,216 consecutive thyroid nodules
    Hye Min Son, Ji-hoon Kim, Soo Chin Kim, Roh-Eul Yoo, Jeong Mo Bae, Hyobin Seo, Dong Gyu Na
    Ultrasonography.2020; 39(2): 159.     CrossRef
  • CT features of thyroid nodules with isolated macrocalcifications detected by ultrasonography
    Wooyul Paik, Dong Gyu Na, Hye Yun Gwon, Jinna Kim
    Ultrasonography.2020; 39(2): 130.     CrossRef
  • Contribution of cytologic examination to diagnosis of poorly differentiated thyroid carcinoma
    Na Rae Kim, Jae Yeon Seok, Yoo Seung Chung, Joon Hyop Lee, Dong Hae Chung
    Journal of Pathology and Translational Medicine.2020; 54(2): 171.     CrossRef
  • Comparison Between Fine Needle Aspiration and Core Needle Biopsy for the Diagnosis of Thyroid Nodules: Effective Indications According to US Findings
    Soo Yeon Hahn, Jung Hee Shin, Young Lyun Oh, Ko Woon Park, Yaeji Lim
    Scientific Reports.2020;[Epub]     CrossRef
  • Thyroid Nodules with Isolated Macrocalcifications: Malignancy Risk of Isolated Macrocalcifications and Postoperative Risk Stratification of Malignant Tumors Manifesting as Isolated Macrocalcifications
    Hye Yun Gwon, Dong Gyu Na, Byeong-Joo Noh, Wooyul Paik, So Jin Yoon, Soo-Jung Choi, Dong Rock Shin
    Korean Journal of Radiology.2020; 21(5): 605.     CrossRef
  • Diagnostic Efficacy and Safety of Core Needle Biopsy as a First-Line Diagnostic Method for Thyroid Nodules: A Prospective Cohort Study
    Min Ji Hong, Dong Gyu Na, Hunkyung Lee
    Thyroid.2020; 30(8): 1141.     CrossRef
  • Re: The 2019 core-needle biopsy practice guidelines
    Ji-hoon Kim
    Ultrasonography.2020; 39(3): 313.     CrossRef
  • Cytopathologic criteria and size should be considered in comparison of fine-needle aspiration vs. core-needle biopsy for thyroid nodules: results based on large surgical series
    Jung Hyun Yoon, Hye Sun Lee, Eun-Kyung Kim, Hee Jung Moon, Vivian Youngjean Park, Jin Young Kwak
    Endocrine.2020; 70(3): 558.     CrossRef
  • Preoperative diagnostic categories of fine needle aspiration cytology for histologically proven thyroid follicular adenoma and carcinoma, and Hurthle cell adenoma and carcinoma: Analysis of cause of under- or misdiagnoses
    Hee Young Na, Jae Hoon Moon, June Young Choi, Hyeong Won Yu, Woo-Jin Jeong, Yeo Koon Kim, Ji-Young Choe, So Yeon Park, Paula Soares
    PLOS ONE.2020; 15(11): e0241597.     CrossRef
  • The Role of Core Needle Biopsy for the Evaluation of Thyroid Nodules with Suspicious Ultrasound Features
    Sae Rom Chung, Jung Hwan Baek, Young Jun Choi, Tae-Yon Sung, Dong Eun Song, Tae Yong Kim, Jeong Hyun Lee
    Korean Journal of Radiology.2019; 20(1): 158.     CrossRef
  • Pathological diagnosis of thyroid nodules based on core needle biopsies: comparative study between core needle biopsies and resected specimens in 578 cases
    Yan Xiong, Limin Yan, Lin Nong, Yalin Zheng, Ting Li
    Diagnostic Pathology.2019;[Epub]     CrossRef
  • The Current Histologic Classification of Thyroid Cancer
    Sylvia L. Asa
    Endocrinology and Metabolism Clinics of North America.2019; 48(1): 1.     CrossRef
  • Core-needle biopsy in thyroid nodules: performance, accuracy, and complications
    Miguel Paja, Jose Luis del Cura, Rosa Zabala, Igone Korta, Aitziber Ugalde, José I. López
    European Radiology.2019; 29(9): 4889.     CrossRef
  • Ultrasound‐guided needle biopsy of large thyroid nodules: Core needle biopsy yields more reliable results than fine needle aspiration
    Hyeon Jin Lee, Young Joong Kim, Hye Yeon Han, Jae Young Seo, Cheol Mog Hwang, KeumWon Kim
    Journal of Clinical Ultrasound.2019; 47(5): 255.     CrossRef
  • Tumor Volume Doubling Time in Active Surveillance of Papillary Thyroid Carcinoma
    Hye-Seon Oh, Hyemi Kwon, Eyun Song, Min Ji Jeon, Tae Yong Kim, Jeong Hyun Lee, Won Bae Kim, Young Kee Shong, Ki-Wook Chung, Jung Hwan Baek, Won Gu Kim
    Thyroid.2019; 29(5): 642.     CrossRef
  • Role of Immunohistochemistry in Fine Needle Aspiration and Core Needle Biopsy of Thyroid Nodules
    Seulki Song, Hyojin Kim, Soon-Hyun Ahn
    Clinical and Experimental Otorhinolaryngology.2019; 12(2): 224.     CrossRef
  • Echogenic foci in thyroid nodules: diagnostic performance with combination of TIRADS and echogenic foci
    Su Min Ha, Yun Jae Chung, Hye Shin Ahn, Jung Hwan Baek, Sung Bin Park
    BMC Medical Imaging.2019;[Epub]     CrossRef
  • Risk of Malignancy According to the Sub-classification of Atypia of Undetermined Significance and Suspicious Follicular Neoplasm Categories in Thyroid Core Needle Biopsies
    Sae Rom Chung, Jung Hwan Baek, Jeong Hyun Lee, Yu-Mi Lee, Tae-Yon Sung, Ki-Wook Chung, Suck Joon Hong, Min Ji Jeon, Tae Yong Kim, Young Kee Shong, Won Bae Kim, Won Gu Kim, Dong Eun Song
    Endocrine Pathology.2019; 30(2): 146.     CrossRef
  • Thyroid core needle biopsy: patients’ pain and satisfaction compared to fine needle aspiration
    Hyo Jin Kim, Yeo Koon Kim, Jae Hoon Moon, June Young Choi, Sang Il Choi
    Endocrine.2019; 65(2): 365.     CrossRef
  • Does Radiofrequency Ablation Induce Neoplastic Changes in Benign Thyroid Nodules: A Preliminary Study
    Su Min Ha, Jun Young Shin, Jung Hwan Baek, Dong Eun Song, Sae Rom Chung, Young Jun Choi, Jeong Hyun Lee
    Endocrinology and Metabolism.2019; 34(2): 169.     CrossRef
  • Differential Diagnosis of Thyroid Follicular Neoplasm from Nodular Hyperplasia by Shear Wave Elastography
    Myung Hi Yoo, Hye Jeong Kim, In Ho Choi, Ji-Oh Mok, Hyeong Kyu Park, Dong Won Byun, Kyoil Suh
    Soonchunhyang Medical Science.2019; 25(1): 10.     CrossRef
  • Ultrasound-Guided Core Needle Biopsy Techniques for Intermediate or Low Suspicion Thyroid Nodules: Which Method is Effective for Diagnosis?
    Soo Yeon Hahn, Jung Hee Shin, Young Lyun Oh, Ko Woon Park
    Korean Journal of Radiology.2019; 20(10): 1454.     CrossRef
  • Preoperative Diagnostic Categories of Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features in Thyroid Core Needle Biopsy and Its Impact on Risk of Malignancy
    Hee Young Na, Ji Won Woo, Jae Hoon Moon, June Young Choi, Woo-Jin Jeong, Yeo Koon Kim, Ji-Young Choe, So Yeon Park
    Endocrine Pathology.2019; 30(4): 329.     CrossRef
  • Utility of a formatted pathologic reporting system in thyroid core needle biopsy: A validation study of 1998 consecutive cases
    Ji‐Young Choe, Yoonjin Kwak, Mimi Kim, Yul Ri Chung, Hyun Jeong Kim, Yeo Koon Kim, So Yeon Park
    Clinical Endocrinology.2018; 88(1): 96.     CrossRef
  • Nuclear features of papillary thyroid carcinoma: Comparison of Core needle biopsy and thyroidectomy specimens
    Jae Yeon Seok, Jungsuk An, Hyun Yee Cho, Younghye Kim, Seung Yeon Ha
    Annals of Diagnostic Pathology.2018; 32: 35.     CrossRef
  • Prevention of total thyroidectomy in noninvasive follicular thyroid neoplasm with papillary‐like nuclear features (NIFTP) based on combined interpretation of ultrasonographic and cytopathologic results
    Sung‐Hye You, Kyu Eun Lee, Roh‐Eul Yoo, Hye Jeong Choi, Kyeong Cheon Jung, Jae‐Kyung Won, Koung Mi Kang, Tae Jin Yoon, Seung Hong Choi, Chul‐Ho Sohn, Ji‐hoon Kim
    Clinical Endocrinology.2018; 88(1): 114.     CrossRef
  • Statement and Recommendations on Interventional Ultrasound as a Thyroid Diagnostic and Treatment Procedure
    Christoph F. Dietrich, Thomas Müller, Jörg Bojunga, Yi Dong, Giovanni Mauri, Maija Radzina, Manjiri Dighe, Xin-Wu Cui, Frank Grünwald, Andreas Schuler, Andre Ignee, Huedayi Korkusuz
    Ultrasound in Medicine & Biology.2018; 44(1): 14.     CrossRef
  • Core needle biopsy of thyroid nodules: outcomes and safety from a large single-center single-operator study
    Jooae Choe, Jung Hwan Baek, Hye Sun Park, Young Jun Choi, Jeong Hyun Lee
    Acta Radiologica.2018; 59(8): 924.     CrossRef
  • Improvement of diagnostic performance of pathologists by reducing the number of pathologists responsible for thyroid fine needle aspiration cytology: An institutional experience
    Jae Yeon Seok, Jungsuk An, Hyun Yee Cho
    Diagnostic Cytopathology.2018; 46(7): 561.     CrossRef
  • Comparison of Immunohistochemistry and Direct Sanger Sequencing for Detection of theBRAFV600EMutation in Thyroid Neoplasm
    Hye-Seon Oh, Hyemi Kwon, Suyeon Park, Mijin Kim, Min Ji Jeon, Tae Yong Kim, Young Kee Shong, Won Bae Kim, Jene Choi, Won Gu Kim, Dong Eun Song
    Endocrinology and Metabolism.2018; 33(1): 62.     CrossRef
  • Comparison of the Diagnostic Efficacy of Ultrasound‐Guided Core Needle Biopsy With 18‐ Versus 20‐Gauge Needles for Thyroid Nodules
    Hye Shin Ahn, Mirinae Seo, Su Min Ha, Hee Sung Kim
    Journal of Ultrasound in Medicine.2018; 37(11): 2565.     CrossRef
  • Impact of Nodule Size on Malignancy Risk Differs according to the Ultrasonography Pattern of Thyroid Nodules
    Min Ji Hong, Dong Gyu Na, Jung Hwan Baek, Jin Yong Sung, Ji-Hoon Kim
    Korean Journal of Radiology.2018; 19(3): 534.     CrossRef
  • Web‐based thyroid imaging reporting and data system: Malignancy risk of atypia of undetermined significance or follicular lesion of undetermined significance thyroid nodules calculated by a combination of ultrasonography features and biopsy results
    Young Jun Choi, Jung Hwan Baek, Jung Hee Shin, Woo Hyun Shim, Seon‐Ok Kim, Won‐Hong Lee, Dong Eun Song, Tae Yong Kim, Ki‐Wook Chung, Jeong Hyun Lee
    Head & Neck.2018; 40(9): 1917.     CrossRef
  • Thyroid Incidentalomas Detected on18F-Fluorodeoxyglucose Positron Emission Tomography with Computed Tomography: Malignant Risk Stratification and Management Plan
    Sae Rom Chung, Young Jun Choi, Chong Hyun Suh, Hwa Jung Kim, Jong Jin Lee, Won Gu Kim, Tae Yon Sung, Yu-mi Lee, Dong Eun Song, Jeong Hyun Lee, Jung Hwan Baek
    Thyroid.2018; 28(6): 762.     CrossRef
  • The role of core needle biopsy in the diagnosis of initially detected thyroid nodules: a systematic review and meta-analysis
    Sae Rom Chung, Chong Hyun Suh, Jung Hwan Baek, Young Jun Choi, Jeong Hyun Lee
    European Radiology.2018; 28(11): 4909.     CrossRef
  • The History of Korean Thyroid Pathology
    Soon Won Hong, Chan Kwon Jung
    International Journal of Thyroidology.2018; 11(1): 15.     CrossRef
  • Ultrasonographic Echogenicity and Histopathologic Correlation of Thyroid Nodules in Core Needle Biopsy Specimens
    Ji-hoon Kim, Dong Gyu Na, Hunkyung Lee
    Korean Journal of Radiology.2018; 19(4): 673.     CrossRef
  • Evaluation of Modified Core-Needle Biopsy in the Diagnosis of Thyroid Nodules
    Soomin Ahn, Sejin Jung, Ji-Ye Kim, Jung Hee Shin, Soo Yeon Hahn, Young Lyun Oh
    Korean Journal of Radiology.2018; 19(4): 656.     CrossRef
  • Role of core needle biopsy as a first-line diagnostic tool for thyroid nodules: a retrospective cohort study
    Min Ji Hong, Dong Gyu Na, Soo Jin Kim, Dae Sik Kim
    Ultrasonography.2018; 37(3): 244.     CrossRef
  • Active Surveillance of Low-Risk Papillary Thyroid Microcarcinoma: A Multi-Center Cohort Study in Korea
    Hye-Seon Oh, Jeonghoon Ha, Hye In Kim, Tae Hyuk Kim, Won Gu Kim, Dong-Jun Lim, Tae Yong Kim, Sun Wook Kim, Won Bae Kim, Young Kee Shong, Jae Hoon Chung, Jung Hwan Baek
    Thyroid.2018; 28(12): 1587.     CrossRef
  • Risk of malignancy according to sub‐classification of the atypia of undetermined significance or follicular lesion of undetermined significance (AUS/FLUS) category in the Bethesda system for reporting thyroid cytopathology
    S. J. Kim, J. Roh, J. H. Baek, S. J. Hong, Y. K. Shong, W. B. Kim, D. E. Song
    Cytopathology.2017; 28(1): 65.     CrossRef
  • Fine-needle aspiration versus core needle biopsy for diagnosis of thyroid malignancy and neoplasm: a matched cohort study
    Soo-Yeon Kim, Hye Sun Lee, Jieun Moon, Eun-Kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Jin Young Kwak
    European Radiology.2017; 27(2): 801.     CrossRef
  • Follicular variant of papillary thyroid carcinoma: comparison of ultrasound-guided core needle biopsy and ultrasound-guided fine needle aspiration in a multicentre study
    Soo Yeon Hahn, Jung Hee Shin, Hyun Kyung Lim, So Lyung Jung
    Clinical Endocrinology.2017; 86(1): 113.     CrossRef
  • Core‐needle biopsy versus repeat fine‐needle aspiration for thyroid nodules initially read as atypia/follicular lesion of undetermined significance
    Young Jun Choi, Jung Hwan Baek, Chong Hyun Suh, Woo Hyun Shim, Boseul Jeong, Jae Kyun Kim, Dong Eun Song, Tae Yong Kim, Ki‐Wook Chung, Jeong Hyun Lee
    Head & Neck.2017; 39(2): 361.     CrossRef
  • Preoperative differentiation between noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and non-NIFTP
    Soo Yeon Hahn, Jung Hee Shin, Hyun Kyung Lim, So Lyung Jung, Young Lyun Oh, In Ho Choi, Chan Kwon Jung
    Clinical Endocrinology.2017; 86(3): 444.     CrossRef
  • Core Needle Biopsy of the Thyroid: 2016 Consensus Statement and Recommendations from Korean Society of Thyroid Radiology
    Dong Gyu Na, Jung Hwan Baek, So Lyung Jung, Ji-hoon Kim, Jin Yong Sung, Kyu Sun Kim, Jeong Hyun Lee, Jung Hee Shin, Yoon Jung Choi, Eun Ju Ha, Hyun Kyung Lim, Soo Jin Kim, Soo Yeon Hahn, Kwang Hwi Lee, Young Jun Choi, Inyoung Youn, Young Joong Kim, Hye Sh
    Korean Journal of Radiology.2017; 18(1): 217.     CrossRef
  • First-Line Use of Core Needle Biopsy for High-Yield Preliminary Diagnosis of Thyroid Nodules
    H.C. Kim, Y.J. Kim, H.Y. Han, J.M. Yi, J.H. Baek, S.Y. Park, J.Y. Seo, K.W. Kim
    American Journal of Neuroradiology.2017; 38(2): 357.     CrossRef
  • Ultrasound-Pathology Discordant Nodules on Core-Needle Biopsy: Malignancy Risk and Management Strategy
    Sae Rom Chung, Jung Hwan Baek, Hye Sun Park, Young Jun Choi, Tae-Yon Sung, Dong Eun Song, Tae Yong Kim, Jeong Hyun Lee
    Thyroid.2017; 27(5): 707.     CrossRef
  • Current status of core needle biopsy of the thyroid
    Jung Hwan Baek
    Ultrasonography.2017; 36(2): 83.     CrossRef
  • Cytology-Ultrasonography Risk-Stratification Scoring System Based on Fine-Needle Aspiration Cytology and the Korean-Thyroid Imaging Reporting and Data System
    Min Ji Hong, Dong Gyu Na, Jung Hwan Baek, Jin Yong Sung, Ji-Hoon Kim
    Thyroid.2017; 27(7): 953.     CrossRef
  • Preoperative clinicopathological characteristics of patients with solitary encapsulated follicular variants of papillary thyroid carcinomas
    Hyemi Kwon, Min Ji Jeon, Jong Ho Yoon, Suck Joon Hong, Jeong Hyun Lee, Tae Yong Kim, Young Kee Shong, Won Bae Kim, Won Gu Kim, Dong Eun Song
    Journal of Surgical Oncology.2017; 116(6): 746.     CrossRef
  • Active Surveillance for Patients With Papillary Thyroid Microcarcinoma: A Single Center’s Experience in Korea
    Hyemi Kwon, Hye-Seon Oh, Mijin Kim, Suyeon Park, Min Ji Jeon, Won Gu Kim, Won Bae Kim, Young Kee Shong, Dong Eun Song, Jung Hwan Baek, Ki-Wook Chung, Tae Yong Kim
    The Journal of Clinical Endocrinology & Metabolism.2017; 102(6): 1917.     CrossRef
  • Comparison of Consecutive Results from Fine Needle Aspiration and Core Needle Biopsy in Thyroid Nodules
    Soon-Hyun Ahn, So-Yeon Park, Sang Il Choi
    Endocrine Pathology.2017; 28(4): 332.     CrossRef
  • Efficacy and safety of core-needle biopsy in initially detected thyroid nodules via propensity score analysis
    Chong Hyun Suh, Jung Hwan Baek, Young Jun Choi, Tae Yong Kim, Tae Yon Sung, Dong Eun Song, Jeong Hyun Lee
    Scientific Reports.2017;[Epub]     CrossRef
  • Comparison of Core-Needle Biopsy and Fine-Needle Aspiration for Evaluating Thyroid Incidentalomas Detected by 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography: A Propensity Score Analysis
    Chong Hyun Suh, Young Jun Choi, Jong Jin Lee, Woo Hyun Shim, Jung Hwan Baek, Han Cheol Chung, Young Kee Shong, Dong Eun Song, Tae Yon Sung, Jeong Hyun Lee
    Thyroid.2017; 27(10): 1258.     CrossRef
  • Thyroid Fine-Needle Aspiration Cytology Practice in Korea
    Yoon Jin Cha, Ju Yeon Pyo, SoonWon Hong, Jae Yeon Seok, Kyung-Ju Kim, Jee-Young Han, Jeong Mo Bae, Hyeong Ju Kwon, Yeejeong Kim, Kyueng-Whan Min, Soonae Oak, Sunhee Chang
    Journal of Pathology and Translational Medicine.2017; 51(6): 521.     CrossRef
  • Recent Advances in Core Needle Biopsy for Thyroid Nodules
    Chan Kwon Jung, Jung Hwan Baek
    Endocrinology and Metabolism.2017; 32(4): 407.     CrossRef
  • Core-needle biopsy for the preoperative diagnosis of follicular neoplasm in thyroid nodule screening: A validation study
    Sung Hak Lee, Gyeong Sin Park, So Lyung Jung, Min-Hee Kim, Ja Seong Bae, Dong Jun Lim, Chan Kwon Jung
    Pathology - Research and Practice.2016; 212(1): 44.     CrossRef
  • The Role of Core-Needle Biopsy as a First-Line Diagnostic Tool for Initially Detected Thyroid Nodules
    Chong Hyun Suh, Jung Hwan Baek, Jeong Hyun Lee, Young Jun Choi, Jae Kyun Kim, Tae-Yon Sung, Jong Ho Yoon, Young Kee Shong
    Thyroid.2016; 26(3): 395.     CrossRef
  • Thyroid nodules with minimal cystic changes have a low risk of malignancy
    Dong Gyu Na, Ji-hoon Kim, Dea Sik Kim, Soo Jin Kim
    Ultrasonography.2016; 35(2): 153.     CrossRef
  • Thyroid nodules with isolated macrocalcification: malignancy risk and diagnostic efficacy of fine-needle aspiration and core needle biopsy
    Dong Gyu Na, Dae Sik Kim, Soo Jin Kim, Jae Wook Ryoo, So Lyung Jung
    Ultrasonography.2016; 35(3): 212.     CrossRef
  • Thyroid Imaging Reporting and Data System Risk Stratification of Thyroid Nodules: Categorization Based on Solidity and Echogenicity
    Dong Gyu Na, Jung Hwan Baek, Jin Yong Sung, Ji-Hoon Kim, Jae Kyun Kim, Young Jun Choi, Hyobin Seo
    Thyroid.2016; 26(4): 562.     CrossRef
  • Fine‐needle aspiration and core needle biopsy: An update on 2 common minimally invasive tissue sampling modalities
    Paul A. VanderLaan
    Cancer Cytopathology.2016; 124(12): 862.     CrossRef
  • The role of core-needle biopsy in the diagnosis of thyroid malignancy in 4580 patients with 4746 thyroid nodules: a systematic review and meta-analysis
    Chong Hyun Suh, Jung Hwan Baek, Jeong Hyun Lee, Young Jun Choi, Kyung Won Kim, Jayoun Lee, Ki-Wook Chung, Young Kee Shong
    Endocrine.2016; 54(2): 315.     CrossRef
  • The Role of Core-Needle Biopsy for Thyroid Nodules with Initially Nondiagnostic Fine-Needle Aspiration Results: A Systematic Review and Meta-Analysis
    Chong Hyun Suh, Jung Hwan Baek, Kyung Won Kim, Tae Yon Sung, Tae Yong Kim, Dong Eun Song, Young Jun Choi, Jeong Hyun Lee
    Endocrine Practice.2016; 22(6): 679.     CrossRef
  • Should Core Needle Biopsy be Used in the Evaluation of Thyroid Nodules?
    Beril Guler, Tugce Kiran, Dilek Sema Arici, Erhan Aysan, Fatma Cavide Sonmez
    Endocrine Pathology.2016; 27(4): 352.     CrossRef
  • A Multicenter Prospective Validation Study for the Korean Thyroid Imaging Reporting and Data System in Patients with Thyroid Nodules
    Eun Ju Ha, Won-Jin Moon, Dong Gyu Na, Young Hen Lee, Nami Choi, Soo Jin Kim, Jae Kyun Kim
    Korean Journal of Radiology.2016; 17(5): 811.     CrossRef
  • Impact of Reclassification on Thyroid Nodules with Architectural Atypia: From Non-Invasive Encapsulated Follicular Variant Papillary Thyroid Carcinomas to Non-Invasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features
    Min Ji Jeon, Dong Eun Song, Chan Kwon Jung, Won Gu Kim, Hyemi Kwon, Yu-Mi Lee, Tae-Yon Sung, Jong Ho Yoon, Ki-Wook Chung, Suck Joon Hong, Jung Hwan Baek, Jeong Hyun Lee, Tae Yong Kim, Young Kee Shong, Won Bae Kim, Rafael Rosell
    PLOS ONE.2016; 11(12): e0167756.     CrossRef
Pathology-MRI Correlation of Hepatocarcinogenesis: Recent Update
Jimi Huh, Kyung Won Kim, Jihun Kim, Eunsil Yu
J Pathol Transl Med. 2015;49(3):218-229.   Published online May 15, 2015
DOI: https://doi.org/10.4132/jptm.2015.04.15
  • 24,062 View
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AbstractAbstract PDF
Understanding the important alterations during hepatocarcinogenesis as well as the characteristic magnetic resonance imaging (MRI) and histopathological features will be helpful for managing patients with chronic liver disease and hepatocellular carcinoma. Recent advances in MRI techniques, such as fat/iron quantification, diffusion-weighted images, and gadoxetic acid-enhanced MRI, have greatly enhanced our understanding of hepatocarcinogenesis.

Citations

Citations to this article as recorded by  
  • Update on Hepatocellular Carcinoma Imaging Features Associated With Histology, Subtype, and Prognosis Along With Changes to LI-RADS in 2024
    Sergio P. Klimkowski, Ann Shi, Omar Altabbakh, Janio Szklaruk, AnuradhaS. Shenoy-Bhangle, Gauruv S. Likhari, Khaled M. Elsayes
    Seminars in Roentgenology.2025; 60(1): 6.     CrossRef
  • Gadoxetic acid in hepatocellular carcinoma and liver metastases: pearls and pitfalls
    H.M. Kwok, C.M. Chau, H.C.H. Lee, T. Wong, H.F. Chan, W.H. Luk, W.T.A. Yung, L.F. Cheng, K.F.J. Ma
    Clinical Radiology.2023; 78(10): 715.     CrossRef
  • Multi-phasic magnetic resonance imaging of hemodynamic interchanges in hepatocarcinogenesis
    Ahmed Mahmoud Elzeneini, Mohsen Ahmed Abdelmohsen, Mohamed Ibrahim Yousef
    Egyptian Journal of Radiology and Nuclear Medicine.2023;[Epub]     CrossRef
  • Risk Factors for Hypervascularization in Hepatobiliary Phase Hypointense Nodules without Arterial Phase Hyperenhancement: A Systematic Review and Meta-analysis
    Tae-Hyung Kim, Sungmin Woo, Sangwon Han, Chong Hyun Suh, Richard Kinh Gian Do, Jeong Min Lee
    Academic Radiology.2022; 29(2): 198.     CrossRef
  • Comparison of IDEAL-IQ and IVIM-DWI for Differentiating between Alpha Fetoprotein-Negative Hepatocellular Carcinoma and Focal Nodular Hyperplasia
    Shaopeng Li, Peng Wang, Jun Qiu, Yiju Xie, Dawei Yin, Kexue Deng
    Oncologie.2022; 24(3): 527.     CrossRef
  • Hepatocarcinogenesis
    Alice Fung, Krishna P. Shanbhogue, Myles T. Taffel, Brian T. Brinkerhoff, Neil D. Theise
    Magnetic Resonance Imaging Clinics of North America.2021; 29(3): 359.     CrossRef
  • Pathologic, Molecular, and Prognostic Radiologic Features of Hepatocellular Carcinoma
    Kathryn J. Fowler, Adam Burgoyne, Tyler J. Fraum, Mojgan Hosseini, Shintaro Ichikawa, Sooah Kim, Azusa Kitao, Jeong Min Lee, Valérie Paradis, Bachir Taouli, Neil D. Theise, Valérie Vilgrain, Jin Wang, Claude B. Sirlin, Victoria Chernyak
    RadioGraphics.2021; 41(6): 1611.     CrossRef
  • Update on Hepatocellular Carcinoma: a Brief Review from Pathologist Standpoint
    Nese Karadag Soylu
    Journal of Gastrointestinal Cancer.2020; 51(4): 1176.     CrossRef
  • Gadoxetate-enhanced dynamic contrast-enhanced MRI for evaluation of liver function and liver fibrosis in preclinical trials
    Jimi Huh, Su Jung Ham, Young Chul Cho, Bumwoo Park, Bohyun Kim, Chul-Woong Woo, Yoonseok Choi, Dong-Cheol Woo, Kyung Won Kim
    BMC Medical Imaging.2019;[Epub]     CrossRef
  • Non-Hypervascular Hypointense Nodules at Gadoxetic Acid MRI: Hepatocellular Carcinoma Risk Assessment with Emphasis on the Role of Diffusion-Weighted Imaging
    Chiara Briani, Marco Di Pietropaolo, Massimo Marignani, Francesco Carbonetti, Paola Begini, Vincenzo David, Elsa Iannicelli
    Journal of Gastrointestinal Cancer.2018; 49(3): 302.     CrossRef
Radiotherapy Response in Microsatellite Instability Related Rectal Cancer
Joo-Shik Shin, Thein Ga Tut, Tao Yang, C. Soon Lee
Korean J Pathol. 2013;47(1):1-8.   Published online February 25, 2013
DOI: https://doi.org/10.4132/KoreanJPathol.2013.47.1.1
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AbstractAbstract PDF

Preoperative radiotherapy may improve the resectability and subsequent local control of rectal cancers. However, the extent of radiation induced regression in these tumours varies widely between individuals. To date no reliable predictive marker of radiation sensitivity in rectal cancer has been identified. At the cellular level, radiation injury initiates a complex molecular network of DNA damage response (DDR) pathways that leads to cell cycle arrest, attempts at re-constituting the damaged DNA and should this fail, then apoptosis. This review presents the details which suggest the roles of DNA mismatch repair proteins, the lack of which define a distinct subset of colorectal cancers with microsatellite instability (MSI), in the DDR pathways. Hence routine assessment of the MSI status in rectal cancers may potentially serve as a predictor of radiotherapy response, thereby improving patient stratification in the administration of this otherwise toxic treatment.

Citations

Citations to this article as recorded by  
  • Neoadjuvant chemoradiotherapy up-regulates PD-L1 in radioresistant colorectal cancer
    Sung Uk Bae, Hye Won Lee, Jee Young Park, Incheol Seo, Jae-Min Cho, Jin Young Kim, Ju Yup Lee, Yoo Jin Lee, Seong Kyu Baek, Nam Kyu Kim, Sang Jun Byun, Shin Kim
    Clinical and Translational Radiation Oncology.2025; 51: 100906.     CrossRef
  • Incidence and Outcomes of Patients With Mismatch Repair Deficient Rectal Cancer Operated in 2016: A Nationwide Cohort From The Netherlands
    Eline G.M. van Geffen, Cornelis R.C. Hogewoning, Sanne-Marije J.A. Hazen, Tania C. Sluckin, Marilyne M. Lange, Petur Snaebjornsson, Regina G.H. Beets-Tan, Corrie A.M. Marijnen, Cornelis Verhoef, Myriam Chalabi, Pieter J. Tanis, Miranda Kusters, Tjeerd S.
    Clinical Colorectal Cancer.2024;[Epub]     CrossRef
  • Potential risks associated with the use of ionizing radiation for imaging and treatment of colorectal cancer in Lynch syndrome patients
    Mingzhu Sun, Jayne Moquet, Michele Ellender, Simon Bouffler, Christophe Badie, Rachel Baldwin-Cleland, Kevin Monahan, Andrew Latchford, David Lloyd, Susan Clark, Nicola A. Anyamene, Elizabeth Ainsbury, David Burling
    Familial Cancer.2023; 22(1): 61.     CrossRef
  • Not All Patients With Locally Advanced Rectal Cancer Benefit From Neoadjuvant Therapy
    Carolyn Chang, Jonathan T. Bliggenstorfer, Jessie Liu, Jennifer Shearer, Paul Dreher, Katherine Bingmer, Sharon L. Stein, Emily Steinhagen
    The American Surgeon™.2023; 89(11): 4327.     CrossRef
  • Survival outcomes in locally advanced dMMR rectal cancer: surgery plus adjunctive treatment vs. surgery alone
    Kemin Ni, Yixiang Zhan, Zhaoce Liu, Zhen Yuan, Shuyuan Wang, Xuan-zhu Zhao, Hangyu Ping, Yaohong Liu, Wanting Wang, Suying Yan, Ran Xin, Qiurong Han, Qinghuai Zhang, Guoxun Li, Xipeng Zhang, Guihua Wang, Zili Zhang, Hong Ma, Chunze Zhang
    BMC Cancer.2023;[Epub]     CrossRef
  • Rectal Cancer in Patients with Hereditary Nonpolyposis Colorectal Cancer Compared with Sporadic Cases: Response to Neoadjuvant Chemoradiation and Local Recurrence
    Khaled M Madbouly, Sameh Hany Emile, Yasmine Amr Issa
    Journal of the American College of Surgeons.2022; 234(5): 793.     CrossRef
  • Molecular Recognition and Quantification of MLH1, MSH2, MSH6, PMS2, and KRAS in Biological Samples
    Raluca-Ioana Stefan-van Staden, Ruxandra-Maria Ilie-Mihai, Maria Coros, Stela Pruneanu
    ECS Sensors Plus.2022; 1(3): 031606.     CrossRef
  • Magnetic Resonance Imaging Downstaging, Pathological Response, and Microsatellite Instability Status in Patients with Signet-Ring Cell Carcinoma Rectum Undergoing Preoperative Long-course Chemoradiation
    B. Rajkrishna, Saikat Das, Dipti Masih, Tharani Putta, Rajat Raghunath, Thomas Samuel Ram
    Current Medical Issues.2022; 20(3): 154.     CrossRef
  • Biomarkers and cell-based models to predict the outcome of neoadjuvant therapy for rectal cancer patients
    Aylin Alkan, Tobias Hofving, Eva Angenete, Ulf Yrlid
    Biomarker Research.2021;[Epub]     CrossRef
  • Microsatellite Instability (MSI) as an Independent Predictor of Pathologic Complete Response (PCR) in Locally Advanced Rectal Cancer
    Shaakir Hasan, Paul Renz, Rodney E. Wegner, Gene Finley, Moses Raj, Dulabh Monga, James McCormick, Alexander Kirichenko
    Annals of Surgery.2020; 271(4): 716.     CrossRef
  • Delivery of Personalized Care for Locally Advanced Rectal Cancer: Incorporating Pathological, Molecular Genetic, and Immunological Biomarkers Into the Multimodal Paradigm
    Éanna J. Ryan, Ben Creavin, Kieran Sheahan
    Frontiers in Oncology.2020;[Epub]     CrossRef
  • Association of mismatch repair status with survival and response to neoadjuvant chemo(radio)therapy in rectal cancer
    Shu-Biao Ye, Yi-Kan Cheng, Lin Zhang, Yi-Feng Zou, Ping Chen, Yan-Hong Deng, Yan Huang, Jian-Hong Peng, Xiao-Jian Wu, Ping Lan
    npj Precision Oncology.2020;[Epub]     CrossRef
  • Implication of expression of MMR proteins and clinicopathological characteristics in gastric cancer
    Renu Verma, Puja Sakhuja, Ritu Srivastava, Prakash Chand Sharma
    Asia-Pacific Journal of Oncology.2020; : 1.     CrossRef
  • Mismatch Repair–Deficient Rectal Cancer and Resistance to Neoadjuvant Chemotherapy
    Andrea Cercek, Gustavo Dos Santos Fernandes, Campbell S. Roxburgh, Karuna Ganesh, Shu Ng, Francisco Sanchez-Vega, Rona Yaeger, Neil H. Segal, Diane L. Reidy-Lagunes, Anna M. Varghese, Arnold Markowitz, Chao Wu, Bryan Szeglin, Charles-Etienne Gabriel Sauvé
    Clinical Cancer Research.2020; 26(13): 3271.     CrossRef
  • The Mismatch Repair System (MMR) in Head and Neck Carcinogenesis and Its Role in Modulating the Response to Immunotherapy: A Critical Review
    Maria Cilona, Luca Giovanni Locatello, Luca Novelli, Oreste Gallo
    Cancers.2020; 12(10): 3006.     CrossRef
  • Genetics of rectal cancer and novel therapies: primer for radiologists
    Sebastian Mondaca, Rona Yaeger
    Abdominal Radiology.2019; 44(11): 3743.     CrossRef
  • Mismatch repair deficiency as a predictive marker for response to adjuvant radiotherapy in endometrial cancer
    Casper Reijnen, Heidi V.N. Küsters-Vandevelde, Clemens F. Prinsen, Leon F.A.G. Massuger, Marc P.M.L. Snijders, Stefan Kommoss, Sara Y. Brucker, Janice S. Kwon, Jessica N. McAlpine, Johanna M.A. Pijnenborg
    Gynecologic Oncology.2019; 154(1): 124.     CrossRef
  • Mismatch Repair System Deficiency Is Associated With Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer
    Nicolas Meillan, Dewi Vernerey, Jérémie H. Lefèvre, Gilles Manceau, Magali Svrcek, Jeremy Augustin, Jean-François Fléjou, Olivier Lascols, Jean-Marc Simon, Romain Cohen, Philippe Maingon, Jean-Baptiste Bachet, Florence Huguet
    International Journal of Radiation Oncology*Biology*Physics.2019; 105(4): 824.     CrossRef
  • Promoter methylation and expression of DNA repair genes MGMT and ERCC1 in tissue and blood of rectal cancer patients
    Sally M. Shalaby, Amal S. El-Shal, Lobna A. Abdelaziz, Eman Abd-Elbary, Mostafa M. Khairy
    Gene.2018; 644: 66.     CrossRef
  • RBBP6 increases radioresistance and serves as a therapeutic target for preoperative radiotherapy in colorectal cancer
    Chao Xiao, Yupeng Wang, Miao Zheng, Jian Chen, Guohe Song, Zhijie Zhou, Chongzhi Zhou, Xing Sun, Lin Zhong, Erxun Ding, Yi Zhang, Liu Yang, Gang Wu, Shifeng Xu, Hong Zhang, Xiaoliang Wang
    Cancer Science.2018; 109(4): 1075.     CrossRef
  • Baseline MAPK signaling activity confers intrinsic radioresistance to KRAS-mutant colorectal carcinoma cells by rapid upregulation of heterogeneous nuclear ribonucleoprotein K (hnRNP K)
    Stefan Eder, Annette Arndt, Andreas Lamkowski, Wassiliki Daskalaki, Alexis Rump, Markus Priller, Felicitas Genze, Eva Wardelmann, Matthias Port, Konrad Steinestel
    Cancer Letters.2017; 385: 160.     CrossRef
  • The current value of determining the mismatch repair status of colorectal cancer: A rationale for routine testing
    E. Ryan, K. Sheahan, B. Creavin, H.M. Mohan, D.C. Winter
    Critical Reviews in Oncology/Hematology.2017; 116: 38.     CrossRef
  • Exploration of Mutation and DNA Methylation of Polo-Like Kinase 1 (PLK1) in Colorectal Cancer
    Wayne Ng, Joo-Shik Shin, Bin Wang, Cheok Soon Lee
    Open Journal of Pathology.2017; 07(03): 45.     CrossRef
  • Expression of Mismatch Repair Proteins in Early and Advanced Gastric Cancer in Poland
    Katarzyna Karpińska-Kaczmarczyk, Magdalena Lewandowska, Małgorzata Ławniczak, Andrzej Białek, Elżbieta Urasińska
    Medical Science Monitor.2016; 22: 2886.     CrossRef
  • DNA Mismatch Repair Deficiency in Rectal Cancer: Benchmarking Its Impact on Prognosis, Neoadjuvant Response Prediction, and Clinical Cancer Genetics
    Nicole de Rosa, Miguel A. Rodriguez-Bigas, George J. Chang, Jula Veerapong, Ester Borras, Sunil Krishnan, Brian Bednarski, Craig A. Messick, John M. Skibber, Barry W. Feig, Patrick M. Lynch, Eduardo Vilar, Y. Nancy You
    Journal of Clinical Oncology.2016; 34(25): 3039.     CrossRef
  • miRNA-148b regulates radioresistance in non-small lung cancer cells via regulation of MutL homologue 1
    Guangsheng Zhai, Gaozhong Li, Bo Xu, Tongfu Jia, Yinping Sun, Jianbo Zheng, Jianbin Li
    Bioscience Reports.2016;[Epub]     CrossRef
  • Predictive and prognostic biomarkers for neoadjuvant chemoradiotherapy in locally advanced rectal cancer
    S.H. Lim, W. Chua, C. Henderson, W. Ng, J.-S. Shin, L. Chantrill, R. Asghari, C.S. Lee, K.J. Spring, P. de Souza
    Critical Reviews in Oncology/Hematology.2015; 96(1): 67.     CrossRef
  • Potential of DNA methylation in rectal cancer as diagnostic and prognostic biomarkers
    Ruth Exner, Walter Pulverer, Martina Diem, Lisa Spaller, Laura Woltering, Martin Schreiber, Brigitte Wolf, Markus Sonntagbauer, Fabian Schröder, Judith Stift, Fritz Wrba, Michael Bergmann, Andreas Weinhäusel, Gerda Egger
    British Journal of Cancer.2015; 113(7): 1035.     CrossRef
  • Study of Liver Transplant Rejection in Alcoholism-Induced Cirrhosis
    J. Tinoco González, C. Bernal Bellido, G. Jimenez Riera, V. Camacho Marente, L.M. Marín Gómez, G. Suárez Artacho, J.M. Álamo Martínez, J. Serrano Díez-Canedo, R.J. Padillo Ruíz, M.A. Gómez Bravo
    Transplantation Proceedings.2013; 45(10): 3650.     CrossRef
Original Articles
Interobserver Variability in Diagnosing High-Grade Neuroendocrine Carcinoma of the Lung and Comparing It with the Morphometric Analysis
Seung Yeon Ha, Joungho Han, Wan-Seop Kim, Byung Seong Suh, Mee Sook Roh
Korean J Pathol. 2012;46(1):42-47.   Published online February 23, 2012
DOI: https://doi.org/10.4132/KoreanJPathol.2012.46.1.42
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AbstractAbstract PDF
Background

Distinguishing small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC) of the lung is difficult with little information about interobserver variability.

Methods

One hundred twenty-nine cases of resected SCLC and LCNEC were independently evaluated by four pathologists and classified according to the 2004 World Health Organization criteria. Agreement was regarded as "unanimous" if all four pathologists agreed on the classification. The kappa statistic was calculated to measure the degree of agreement between pathologists. We also measured cell size using image analysis, and receiver-operating-characteristic curve analysis was performed to evaluate cell size in predicting the diagnosis of high-grade neuroendocrine (NE) carcinomas in 66 cases.

Results

Unanimous agreement was achieved in 55.0% of 129 cases. The kappa values ranged from 0.35 to 0.81. Morphometric analysis reaffirmed that there was a continuous spectrum of cell size from SCLC to LCNEC and showed that tumors with cells falling in the middle size range were difficult to categorize and lacked unanimous agreement.

Conclusions

Our results provide an objective explanation for considerable interobserver variability in the diagnosis of high-grade pulmonary NE carcinomas. Further studies would need to define more stringent and objective definitions of cytologic and architectural characteristics to reliably distinguish between SCLC and LCNEC.

Citations

Citations to this article as recorded by  
  • Case report: A patient with EGFR L861Q positive adenosquamous lung carcinoma transforming into large cell neuroendocrine cancer after treatment with Almonertinib
    Kele Cheng, Yong Zhu, Ran Sang, Zhongsheng Kuang, Yang Cao
    Frontiers in Oncology.2025;[Epub]     CrossRef
  • Updates on lung neuroendocrine neoplasm classification
    Giulia Vocino Trucco, Luisella Righi, Marco Volante, Mauro Papotti
    Histopathology.2024; 84(1): 67.     CrossRef
  • Recent advancement of HDAC inhibitors against breast cancer
    Syed Abdulla Mehmood, Kantrol Kumar Sahu, Sounok Sengupta, Sangh Partap, Rajshekhar Karpoormath, Brajesh Kumar, Deepak Kumar
    Medical Oncology.2023;[Epub]     CrossRef
  • Genomic Feature of a Rare Case of Mix Small-Cell and Large-Cell Neuroendocrine Lung Carcinoma: A Case Report
    Youcai Zhu, Feng Zhang, Dong Yu, Fang Wang, Manxiang Yin, Liangye Chen, Chun Xiao, Yueyan Huang, Feng Ding
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Small-Cell Carcinoma of the Lung: What We Learned about It?
    Luisella Righi, Marco Volante, Mauro Papotti
    Acta Cytologica.2022; 66(4): 257.     CrossRef
  • Hierarchical identification of a transcriptional panel for the histological diagnosis of lung neuroendocrine tumors
    Juxuan Zhang, Jiaxing Deng, Xiao Feng, Yilong Tan, Xin Li, Yixin Liu, Mengyue Li, Haitao Qi, Lefan Tang, Qingwei Meng, Haidan Yan, Lishuang Qi
    Frontiers in Genetics.2022;[Epub]     CrossRef
  • Immunohistochemical Staining With Neuroendocrine Markers is Essential in the Diagnosis of Neuroendocrine Neoplasms of the Esophagogastric Junction
    Dea N.M. Jepsen, Anne-Marie K. Fiehn, Rajendra S. Garbyal, Ulla Engel, Jakob Holm, Birgitte Federspiel
    Applied Immunohistochemistry & Molecular Morphology.2021; 29(6): 454.     CrossRef
  • Improving differential diagnosis of pulmonary large cell neuroendocrine carcinoma and small cell lung cancer via a transcriptomic, biological pathway-based machine learning model
    Junhong Guo, Likun Hou, Wei Zhang, Zhengwei Dong, Lei Zhang, Chunyan Wu
    Translational Oncology.2021; 14(12): 101222.     CrossRef
  • Are Neuroendocrine Negative Small Cell Lung Cancer and Large Cell Neuroendocrine Carcinoma with WT RB1 two Faces of the Same Entity?
    Dmitriy Sonkin, Anish Thomas, Beverly A Teicher
    Lung Cancer Management.2019;[Epub]     CrossRef
  • Ki-67 labeling index of neuroendocrine tumors of the lung has a high level of correspondence between biopsy samples and surgical specimens when strict counting guidelines are applied
    Alessandra Fabbri, Mara Cossa, Angelica Sonzogni, Mauro Papotti, Luisella Righi, Gaia Gatti, Patrick Maisonneuve, Barbara Valeri, Ugo Pastorino, Giuseppe Pelosi
    Virchows Archiv.2017; 470(2): 153.     CrossRef
  • The Use of Immunohistochemistry Improves the Diagnosis of Small Cell Lung Cancer and Its Differential Diagnosis. An International Reproducibility Study in a Demanding Set of Cases
    Erik Thunnissen, Alain C. Borczuk, Douglas B. Flieder, Birgit Witte, Mary Beth Beasley, Jin-Haeng Chung, Sanja Dacic, Sylvie Lantuejoul, Prudence A. Russell, Michael den Bakker, Johan Botling, Elisabeth Brambilla, Erienne de Cuba, Kim R. Geisinger, Kenzo
    Journal of Thoracic Oncology.2017; 12(2): 334.     CrossRef
  • Reply to Letter “The Use of Immunohistochemistry Improves the Diagnosis of Small Cell Lung Cancer and Its Differential Diagnosis. An International Reproducibility Study in a Demanding Set of Cases.”
    Erik Thunnissen, Birgit I. Witte, Masayuki Noguchi, Yasushi Yatabe
    Journal of Thoracic Oncology.2017; 12(6): e70.     CrossRef
  • What clinicians are asking pathologists when dealing with lung neuroendocrine neoplasms?
    Giuseppe Pelosi, Alessandra Fabbri, Mara Cossa, Angelica Sonzogni, Barbara Valeri, Luisella Righi, Mauro Papotti
    Seminars in Diagnostic Pathology.2015; 32(6): 469.     CrossRef
  • Unraveling Tumor Grading and Genomic Landscape in Lung Neuroendocrine Tumors
    Giuseppe Pelosi, Mauro Papotti, Guido Rindi, Aldo Scarpa
    Endocrine Pathology.2014; 25(2): 151.     CrossRef
  • Grading the neuroendocrine tumors of the lung: an evidence-based proposal
    G Rindi, C Klersy, F Inzani, G Fellegara, L Ampollini, A Ardizzoni, N Campanini, P Carbognani, T M De Pas, D Galetta, P L Granone, L Righi, M Rusca, L Spaggiari, M Tiseo, G Viale, M Volante, M Papotti, G Pelosi
    Endocrine-Related Cancer.2014; 21(1): 1.     CrossRef
  • Controversial issues and new discoveries in lung neuroendocrine tumors
    Giuseppe Pelosi, Kenzo Hiroshima, Mari Mino-Kenudson
    Diagnostic Histopathology.2014; 20(10): 392.     CrossRef
  • BAI3, CDX2 and VIL1: a panel of three antibodies to distinguish small cell from large cell neuroendocrine lung carcinomas
    Muhammad F Bari, Helen Brown, Andrew G Nicholson, Keith M Kerr, John R Gosney, William A Wallace, Irshad Soomro, Salli Muller, Danielle Peat, Jonathan D Moore, Lesley A Ward, Maxim B Freidin, Eric Lim, Manu Vatish, David R J Snead
    Histopathology.2014; 64(4): 547.     CrossRef
  • Neuroendocrine tumours—challenges in the diagnosis and classification of pulmonary neuroendocrine tumours
    M A den Bakker, F B J M Thunnissen
    Journal of Clinical Pathology.2013; 66(10): 862.     CrossRef
  • Morphologic Analysis of Pulmonary Neuroendocrine Tumors
    Seung Seok Lee, Myunghee Kang, Seung Yeon Ha, Jungsuk An, Mee Sook Roh, Chang Won Ha, Jungho Han
    Korean Journal of Pathology.2013; 47(1): 16.     CrossRef
  • Altered expression of microRNA miR‐21, miR‐155, and let‐7a and their roles in pulmonary neuroendocrine tumors
    Hyoun Wook Lee, Eun Hee Lee, Seung Yeon Ha, Chang Hun Lee, Hee Kyung Chang, Sunhee Chang, Kun Young Kwon, Il Seon Hwang, Mee Sook Roh, Jeong Wook Seo
    Pathology International.2012; 62(9): 583.     CrossRef
Nuclear Image Analysis Study of Neuroendocrine Tumors
Meeja Park, Taehwa Baek, Jongho Baek, Hyunjin Son, Dongwook Kang, Jooheon Kim, Hyekyung Lee
Korean J Pathol. 2012;46(1):38-41.   Published online February 23, 2012
DOI: https://doi.org/10.4132/KoreanJPathol.2012.46.1.38
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AbstractAbstract PDF
Background

There is a subjective disagreement about nuclear chromatin in the field of pathology. Objective values of red, green, and blue (RGB) light intensities for nuclear chromatin can be obtained through a quantitative analysis using digital images.

Methods

We examined 10 cases of well differentiated neuroendocrine tumors of the rectum, small cell lung carcinomas, and moderately differentiated squamous cell lung carcinomas respectively. For each case, we selected 30 representative cells and captured typical microscopic findings. Using an image analyzer, we determined the longest nuclear line profiles and obtained graph files and Excel data on RGB light intensities. We assessed the meaningful differences in graph files and Excel data among the three different tumors.

Results

The nucleus of hematoxylin and eosin-stained tumor cells was expressed as a combination of RGB light sources. The highest intensity was from blue, whereas the lowest intensity was from green. According to the graph files, green showed the most noticeable change in the light intensity, which is consistent with the difference in standard deviations.

Conclusions

The change in the light intensity for green has an important implication for differentiating between tumors. Specific features of the nucleus can be expressed in specific values of RGB light intensities.

Citations

Citations to this article as recorded by  
  • Difference of the Nuclear Green Light Intensity between Papillary Carcinoma Cells Showing Clear Nuclei and Non-neoplastic Follicular Epithelia in Papillary Thyroid Carcinoma
    Hyekyung Lee, Tae Hwa Baek, Meeja Park, Seung Yun Lee, Hyun Jin Son, Dong Wook Kang, Joo Heon Kim, Soo Young Kim
    Journal of Pathology and Translational Medicine.2016; 50(5): 355.     CrossRef
  • Comparison of diagnostic accuracy between CellprepPlus® and ThinPrep® liquid‐based preparations in effusion cytology
    Yong‐Moon Lee, Ji‐Yong Hwang, Seung‐Myoung Son, Song‐Yi Choi, Ho‐Chang Lee, Eun‐Joong Kim, Hye‐Suk Han, Jin young An, Joung‐Ho Han, Ok‐Jun Lee
    Diagnostic Cytopathology.2014; 42(5): 384.     CrossRef
Morphometric Analysis for Pulmonary Small Cell Carcinoma Using Image Analysis.
Sun Min Jeong, Seung Yeon Ha, Jungsuk An, Hyun Yee Cho, Dong Hae Chung, Na Rae Kim, Sanghui Park
Korean J Pathol. 2011;45(1):87-91.
DOI: https://doi.org/10.4132/KoreanJPathol.2011.45.1.87
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  • 1 Crossref
AbstractAbstract PDF
BACKGROUND
There are few studies of how to diagnose small cell lung cancer in cytological tests through morphometric analysis. We tried to measure and analyze characteristics of small cell carcinoma in lung by image analysis.
METHODS
We studied three types of cytologic specimens from 89 patients who were diagnosed with small cell lung cancer by immunohistochemistry. We measured area, perimeter, maximal length and maximal width of cells from small cell carcinoma using image analysis.
RESULTS
In lung aspirates, the nuclear mean area, perimeter, maximal length and maximal width of small cell lung cancer were 218.69 microm2, 55 microm, 18.48 microm and 14.65 microm. In bronchial washings, nuclear measurements were 194.66 microm2, 50.07 microm, 16.27 microm and 14.1 microm. In pleural fluid, values were 177.85 microm2, 48.09 microm, 15.7 microm and 13.37 microm.
CONCLUSIONS
Nuclear size of small cell lung carcinoma is variable and depends on the cytology method. Nuclei are spindle-shaped and larger in small cell carcinoma from lung aspirates than in bronchial washings or pleural fluid. The cytoplasms of the cells in bronchial washings and pleural fluid were swollen. Therefore, one should consider morphologic changes when trying to diagnose small cell lung cancer through cytological tests.

Citations

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  • Interobserver Variability in Diagnosing High-Grade Neuroendocrine Carcinoma of the Lung and Comparing It with the Morphometric Analysis
    Seung Yeon Ha, Joungho Han, Wan-Seop Kim, Byung Seong Suh, Mee Sook Roh
    Korean Journal of Pathology.2012; 46(1): 42.     CrossRef
Detecting Malignant Urothelial Cells by Morphometric Analysis of ThinPrep(R) Liquid-based Urine Cytology Specimens.
Bong Kyung Shin, Young Suk Lee, Hoiseon Jeong, Sang Ho Lee, Hyunchul Kim, Aree Kim, Insun Kim, Han Kyeom Kim
Korean J Cytopathol. 2008;19(2):136-143.
DOI: https://doi.org/10.3338/kjc.2008.19.2.136
  • 2,879 View
  • 23 Download
  • 5 Crossref
AbstractAbstract PDF
Urothelial carcinoma accounts for 90% of all the cases of bladder cancer. Although many cases can be easily managed by local excision, urothelial carcinoma rather frequently recurs, tends to progress to muscle invasion, and requires regular follow-ups. Urine cytology is a main approach for the follow-up of bladder tumors. It is noninvasive, but it has low sensitivity of around 50% with using the conventional cytospin preparation. Liquid-based cytology (LBC) has been developed as a replacement for the conventional technique. We compared the cytomorphometric parameters of ThinPrep(R) and cytospin preparation urine cytology to see whether there are definite differences between the two methods and which technique allows malignant cells to be more effectively discriminated from benign cells. The nuclear-to-cytoplasmic ratio value, as measured by digital image analysis, was efficient for differentiating malignant and benign urothelial cells, and this was irrespective of the preparation method and the tumor grade. Neither the ThinPrep(R) nor the conventional preparation cytology was definitely superior for distinguishing malignant cells from benign cells by cytomorphometric analysis of the adequately preserved cells. However, the ThinPrep(R) preparation showed significant advantages when considering the better preservation and cellularity with a clear background.

Citations

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  • Utility of Image Morphometry in the Atypical Urothelial Cells and High-Grade Urothelial Carcinoma Categories of the Paris System for Reporting Urinary Cytology
    K.C. Sharan, Manish Rohilla, Pranab Dey, Radhika Srinivasan, Nandita Kakkar, Ravimohan S. Mavuduru
    Journal of Cytology.2024; 41(3): 137.     CrossRef
  • Comparison of diagnostic accuracy between CellprepPlus® and ThinPrep® liquid‐based preparations in effusion cytology
    Yong‐Moon Lee, Ji‐Yong Hwang, Seung‐Myoung Son, Song‐Yi Choi, Ho‐Chang Lee, Eun‐Joong Kim, Hye‐Suk Han, Jin young An, Joung‐Ho Han, Ok‐Jun Lee
    Diagnostic Cytopathology.2014; 42(5): 384.     CrossRef
  • A Comparison Between ThinPrep Monolayer and Cytospin Cytology for the Detection of Bladder Cancer
    Ji Yong Kim, Hyung Jin Kim
    Korean Journal of Urology.2014; 55(6): 390.     CrossRef
  • Cytological and Morphometric Study of Urinary Epithelial Cells with Histopathological Correlation
    Asim Kumar Manna, Manisha Sarkar, Ujjal Bandyopadhyay, Srabani Chakrabarti, Swapan Pathak, Diptendra Kumar Sarkar
    Indian Journal of Surgery.2014; 76(1): 26.     CrossRef
  • Evaluation of Urine Cytology in Urothelial Carcinoma Patients: A Comparison of CellprepPlus® Liquid-Based Cytology and Conventional Smear
    Seung-Myoung Son, Ji Hae Koo, Song-Yi Choi, Ho-Chang Lee, Yong-Moon Lee, Hyung Geun Song, Hae-Kyung Hwang, Hye-Suk Han, Seok-Joong Yun, Wun-Jae Kim, Eun-Joong Kim, Ok-Jun Lee
    Korean Journal of Pathology.2012; 46(1): 68.     CrossRef
Case Report
Pneumocystis carinii Pneumonia Presented as Diffuse Alveolar Damage: Report of a case.
Sook Kim, Jeong Ja Kwak, Dong Won Kim, So Young Jin, Dong Wha Lee
Korean J Pathol. 1996;30(12):1155-1158.
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Pneumocystis carinii is the most common cause of diffuse pulmonary infiltrates in the immunocompromised patients. Microscopically, Pneumocystis carinii pneumonia(PCP) shows characteristic frothy intraalveolar exudate and interstitial lymphocytic and plasma cell infiltrate. However, sometimes the only histologic finding of PCP on routine hematoxylin-eosin stain is that of diffuse alveolar damage(DAD), when we can miss the diagnosis without aid of special stains. We report a case of Pneumocystis carinii pneumonia presenting as DAD in a 50-year old man after chemotherapy due to malignant lymphoma. Open lung biopsy specimen reveals the early stage of DAD without any characteristic findings, such as foamy exudate. However many cysts of Pneumocystis carinii were found on Gomori's methenamine silver(GMS) stain. Therefore, GMS stain should be routinely performed on all biopsy specimens obtained from immunocompromised patients.
Original Article
The Effect of Ginseng Saponin on the Dopaminergic Neurons in the Parkinson's Disease Model in Mice.
Chang Ok Kim, Ki Sok Kim, Young Buhm Huh, Byeong Woo Ahn, Beom Seok Han, Kwang Sik Choi, Ki Yul Nam, Sang Woo Juhng
Korean J Pathol. 1997;31(9):805-814.
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AbstractAbstract PDF
Saponin has been known to be a major antioxidant component in panax ginseng. Recent experimental study suggests that some antioxidant materials prevent Parkinson's disease caused by 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP) in an animal model. The present study was performed to demonstrate the effect of ginseng saponins in the Parkinson's disease model induced by MPTP. To verify the effect of ginseng saponin on dopaminergic neurons in the mice brain, the tyrosine hydroxylase-immunoreactive (TH-ir) neurons were observed by immunohistochemical stain and immunoelectron microscopy (preembedding method). Also, in order to estimate the immunoreactivity of dopaminergic neuropils, they were quantified by image analysis. The number of TH-ir neurons of substantia nigra was significantly increased in the high-dose (0.46 mg/kg) ginseng saponin group compared with the MPTP injected group. The immunoreactivity of TH-ir neuropils in striatum was significantly increased in both high and low-dose (0.1 mg/kg) ginseng saponin groups compared with the MPTP injected group. In immunoelectron microscopic observation, TH-ir neurons of the control and both ginseng saponin injected group showed normal nuclei and well preserved cytoplasmic organelles. In the MPTP injected group, dying dopaminergic neurons showed destroyed nuclei and cytoplasmic organelles. These results suggest that ginseng saponin has a protective effect on the Parkinson's disease model induced by MPTP.

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