Skip Navigation
Skip to contents

J Pathol Transl Med : Journal of Pathology and Translational Medicine

OPEN ACCESS
SEARCH
Search

Search

Page Path
HOME > Search
14 "Image analysis"
Filter
Filter
Article category
Keywords
Publication year
Authors
Funded articles
Review
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
  • 23,064 View
  • 1,162 Download
  • 102 Web of Science
  • 113 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.

Citations

Citations to this article as recorded by  
  • 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
  • The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems
    Noa Hurvitz, Yaron Ilan
    Clinics and Practice.2023; 13(4): 994.     CrossRef
  • Building a nonclinical pathology laboratory of the future for pharmaceutical research excellence
    D.G. Rudmann, L. Bertrand, A. Zuraw, J. Deiters, M. Staup, Y. Rivenson, J. Kuklyte
    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
  • 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.2023;[Epub]     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
  • 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
  • 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
  • Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology
    Daniel Royston, Adam J. Mead, Bethan Psaila
    Hematology/Oncology Clinics of North America.2021; 35(2): 279.     CrossRef
  • Idiosyncratic Drug-Induced Liver Injury (DILI) and Herb-Induced Liver Injury (HILI): Diagnostic Algorithm Based on the Quantitative Roussel Uclaf Causality Assessment Method (RUCAM)
    Rolf Teschke, Gaby Danan
    Diagnostics.2021; 11(3): 458.     CrossRef
  • Searching Images for Consensus
    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
  • Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network
    Yao Yao, Shuiping Gou, Ru Tian, Xiangrong Zhang, Shuixiang He, Zhiguo Zhou
    BioMed Research International.2021; 2021: 1.     CrossRef
  • Artificial intelligence and sleep: Advancing sleep medicine
    Nathaniel F. Watson, Christopher R. Fernandez
    Sleep Medicine Reviews.2021; 59: 101512.     CrossRef
  • Prospective Of Artificial Intelligence: Emerging Trends In Modern Biosciences Research
    Pradeep Kumar, Ajit Kumar Singh Yadav, Abhishek Singh
    IOP Conference Series: Materials Science and Engineering.2021; 1020(1): 012008.     CrossRef
  • Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives
    Simon Lennartz, Thomas Dratsch, David Zopfs, Thorsten Persigehl, David Maintz, Nils Große Hokamp, Daniel Pinto dos Santos
    Journal of Medical Internet Research.2021; 23(2): e24221.     CrossRef
  • HEAL: an automated deep learning framework for cancer histopathology image analysis
    Yanan Wang, Nicolas Coudray, Yun Zhao, Fuyi Li, Changyuan Hu, Yao-Zhong Zhang, Seiya Imoto, Aristotelis Tsirigos, Geoffrey I Webb, Roger J Daly, Jiangning Song, Zhiyong Lu
    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
  • Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
    Xiaoliang Xie, Xulin Wang, Yuebin Liang, Jingya Yang, Yan Wu, Li Li, Xin Sun, Pingping Bing, Binsheng He, Geng Tian, Xiaoli Shi
    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
  • A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
    Sarah N. Dudgeon, Si Wen, Matthew G. Hanna, Rajarsi Gupta, Mohamed Amgad, Manasi Sheth, Hetal Marble, Richard Huang, Markus D. Herrmann, Clifford H. Szu, Darick Tong, Bruce Werness, Evan Szu, Denis Larsimont, Anant Madabhushi, Evangelos Hytopoulos, Weijie
    Journal of Pathology Informatics.2021; 12(1): 45.     CrossRef
  • Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students' Perception
    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
    The Korean Journal of Physiology & Pharmacology.2020; 24(1): 89.     CrossRef
  • Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations
    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
  • Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches
    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
    Database.2020;[Epub]     CrossRef
  • Scoring pleurisy in slaughtered pigs using convolutional neural networks
    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
  • Current Status of Computational Intelligence Applications in Dermatological Clinical Practice
    Carmen Rodríguez-Cerdeira, José Luís González-Cespón, Roberto Arenas
    The Open Dermatology Journal.2020; 14(1): 6.     CrossRef
  • New unified insights on deep learning in radiological and pathological images: Beyond quantitative performances to qualitative interpretation
    Yoichi Hayashi
    Informatics in Medicine Unlocked.2020; 19: 100329.     CrossRef
  • Artificial Intelligence in Cardiology: Present and Future
    Francisco Lopez-Jimenez, Zachi Attia, Adelaide M. Arruda-Olson, Rickey Carter, Panithaya Chareonthaitawee, Hayan Jouni, Suraj Kapa, Amir Lerman, Christina Luong, Jose R. Medina-Inojosa, Peter A. Noseworthy, Patricia A. Pellikka, Margaret M. Redfield, Vero
    Mayo Clinic Proceedings.2020; 95(5): 1015.     CrossRef
  • Artificial intelligence in oncology
    Hideyuki Shimizu, Keiichi I. Nakayama
    Cancer Science.2020; 111(5): 1452.     CrossRef
  • Artificial intelligence and the future of global health
    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
  • Weakly-supervised learning for lung carcinoma classification using deep learning
    Fahdi Kanavati, Gouji Toyokawa, Seiya Momosaki, Michael Rambeau, Yuka Kozuma, Fumihiro Shoji, Koji Yamazaki, Sadanori Takeo, Osamu Iizuka, Masayuki Tsuneki
    Scientific Reports.2020;[Epub]     CrossRef
  • The use of artificial intelligence, machine learning and deep learning in oncologic histopathology
    Ahmed S. Sultan, Mohamed A. Elgharib, Tiffany Tavares, Maryam Jessri, John R. Basile
    Journal of Oral Pathology & Medicine.2020; 49(9): 849.     CrossRef
  • Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions
    Anil V. Parwani, Mahul B. Amin
    Advances in Anatomic Pathology.2020; 27(4): 221.     CrossRef
  • Advances in tissue-based imaging: impact on oncology research and clinical practice
    Arman Rahman, Chowdhury Jahangir, Seodhna M. Lynch, Nebras Alattar, Claudia Aura, Niamh Russell, Fiona Lanigan, William M. Gallagher
    Expert Review of Molecular Diagnostics.2020; 20(10): 1027.     CrossRef
  • Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review
    Nishant Thakur, Hongjun Yoon, Yosep Chong
    Cancers.2020; 12(7): 1884.     CrossRef
  • Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit
    Farah Deshmukh, Shamel S. Merchant
    American Journal of Gastroenterology.2020; 115(10): 1657.     CrossRef
  • Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning
    Hyun-Jong Jang, Ahwon Lee, J Kang, In Hye Song, Sung Hak Lee
    World Journal of Gastroenterology.2020; 26(40): 6207.     CrossRef
  • Application of system analysis methods for modeling the development of hand-arm vibration syndrome: problems and approaches to solution
    M P Diakovich, M V Krivov
    Journal of Physics: Conference Series.2020; 1661(1): 012029.     CrossRef
  • Histo-ELISA technique for quantification and localization of tissue components
    Zhongmin Li, Silvia Goebel, Andreas Reimann, Martin Ungerer
    Scientific Reports.2020;[Epub]     CrossRef
  • Role of artificial intelligence in diagnostic oral pathology-A modern approach
    AyinampudiBhargavi Krishna, Azra Tanveer, PanchaVenkat Bhagirath, Ashalata Gannepalli
    Journal of Oral and Maxillofacial Pathology.2020; 24(1): 152.     CrossRef
  • Applications of deep learning for the analysis of medical data
    Hyun-Jong Jang, Kyung-Ok Cho
    Archives of Pharmacal Research.2019; 42(6): 492.     CrossRef
  • PROMISE CLIP Project: A Retrospective, Multicenter Study for Prostate Cancer that Integrates Clinical, Imaging and Pathology Data
    Jihwan Park, Mi Jung Rho, Yong Hyun Park, Chan Kwon Jung, Yosep Chong, Choung-Soo Kim, Heounjeong Go, Seong Soo Jeon, Minyong Kang, Hak Jong Lee, Sung Il Hwang, Ji Youl Lee
    Applied Sciences.2019; 9(15): 2982.     CrossRef
  • Key challenges for delivering clinical impact with artificial intelligence
    Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, Dominic King
    BMC Medicine.2019;[Epub]     CrossRef
  • Deep Learning for Whole Slide Image Analysis: An Overview
    Neofytos Dimitriou, Ognjen Arandjelović, Peter D. Caie
    Frontiers in Medicine.2019;[Epub]     CrossRef
  • Barriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review
    Mir Mohammed Assadullah
    SSRN Electronic Journal .2019;[Epub]     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
  • 7,216 View
  • 44 Download
  • 19 Crossref
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  
  • 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; 8(2): LMT13.     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
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
  • 3,166 View
  • 26 Download
  • 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

Citations to this article as recorded by  
  • 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
  • 2,284 View
  • 17 Download
  • 4 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

Citations to this article as recorded by  
  • 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
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.
  • 1,960 View
  • 25 Download
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.
Image Standardization and Determination of Gray Level Threshold in the Assessment of the Myocardial Fibrosis by the Computerized Image Analysis.
Nam Young Lee, Young Sik Park, Jin Haeng Chung, Jeong Wook Seo
Korean J Pathol. 1998;32(7):494-503.
  • 1,416 View
  • 10 Download
AbstractAbstract
The computerized image analysis is a useful tool for the quantitative assessment of histopathologic findings. In contrast to the usual microscopic examination by pathologists, the computerization should be accompanied with the standardization process of the image. We developed an algorithm to standardize images and to determine the optimal gray level threshold, using a myocardial fibrosis model. Sirius red staining was more convenient for the image analysis than Masson's trichrome staining because of a better contrast with the surrounding structures. To get an optimal measurement, light intensity was standardized at each of the fibrosis, myocardium and background. In this study, the most promising method to determine the degree of fibrosis was that of revising the background without tissue to a gray level of 200, obtaining a green component of the color image, revising the myocardial fiber to 163, and defining a partial ratio as fibrosis index when the gray level threshold was 120. These threshold levels and parameters were determined after drawing the binarization index curves according to the change of the gray level threshold and by the morphological examination of the actual binarization figures overlaid to the original color image. Through these processes we could get a consistent result on the myocardial fibrosis and we expect a similar principle applies when we analyze color images in the histopathologic quantitation by computerized image analysis.
Image Analysis of Glomerular Changes in Patients with Post-transplant IgA Nephropathy.
Kye Won Kwon, Hyeon Joo Jeong
Korean J Pathol. 2001;35(3):206-211.
  • 1,525 View
  • 13 Download
AbstractAbstract PDF
BACKGROUND
IgA nephropathy after renal transplantation (post-transplant IgAN) may recapitulate the IgAN of native kidneys, however, little has been reported about the histologic characteristics. The aim of this study is to apply glomerular morphometry using an image analyser to examine the histologic characteristics of post-transplant IgAN.
METHODS
The outer margin of the glomerulus (Bowman's area, BA) and glomerular tuft area (GA) were traced manually. The measured area were automatically calculated by KS300 image analysis system (Kontron, Munchen, Germany). The mesangial area (MA) was calculated with a summing each manually traced mesangial area. The total number of glomerular (GC) and mesangial cells (MC) were counted. Eight cases of renal section obtained by nephrectomy due to renal cell carcinoma (normal control: N-CTRL) and nineteen cases of renal section obtained from post-transplantation patients without IgAN (transplantation control: Tx-CTRL) served as controls.
RESULTS
A total of 35 biopsies were finally selected for measurement. BA and GA of post-transplant IgAN were 1.6 and 1.4 times larger than the N-CTRL, respectively, and were not significantly different from Tx-CTRL. MA was 1.4 times significantly larger than that of the Tx-CTRL. As compared to that of the N-CTRL, it was 1.2 times larger, but this difference was not statistically significant. The GC and MC of post-transplant IgAN and the Tx-CTRL were significantly lower than the N-CTRL. There were no significant correlations between glomerular hypertrophy and duration after renal transplantation, mesangial changes, segmental sclerosis, or degree of renal cortical interstitial fibrosis in post-transplant IgAN.
CONCLUSIONS
Prominent glomerular hypertrophy and mesangial expansion suggest a hyperfiltration injury in post-transplant IgAN and a possible way to glomerulosclerosis.
DNA Ploidy in Anaplastic Carcinoma of the Thyroid Gland by Image Analysis.
Ji Shin Lee, Min Cheol Lee, Chang Soo Park, Sang Woo Juhng
Korean J Cytopathol. 1995;6(1):10-17.
  • 1,483 View
  • 11 Download
AbstractAbstract PDF
Anaplastic carcinoma of the thyroid gland is one of the most malignant tumors. Recently, DNA ploidy measured by flow cytometry and image analysis has been suggested as an additional useful indicator of tumor behavior. Studies on the occurrence and clinical significance of DNA aneuploidy in anaplastic carcinoma of the thyroid are rare. In this study, the pattern of DNA ploidy was measured by image analysis on Papanicolaou stained slides in four cases of anaplastic carcinoma and also measured by flow cytometry using paraffin blocks in two cases. In all cases of anaplastic carcinoma. DNA aneuploidy was found by image analaysis. By flow cytometry, one case had a diploid peak and the other case had an arieuploid peak. According to the above results, we conclude that anaplastic carcinoma of the thyroid glands have a high incidence of DNA aneuploidy and image analysis using Papanicolaou stained slides is a useful method in detecting DNA aneuploidy.
A study of Digital Image Analysis of Chromatin Texture for Discrimination of Thyroid Neoplastic Cells.
Sang Woo Juhng, Jae Hyuk Lee, Eun Kyung Bum, Chang Won Kim
Korean J Cytopathol. 1996;7(1):23-30.
  • 1,434 View
  • 16 Download
AbstractAbstract PDF
Chromatin texture, which partly reflects nuclear organization, is evolving as an important parameter indicating cell activation or transformation. In this study, chromatin pattern was evaluated by image analysis of the electron micrographs of follicular and papillary carcinoma cells of the thyroid gland and tested for discrimination of the two neoplasms. Digital grey images were converted from the electron micrographs; nuclear images, excluding nucleolus and intranuclear cytoplasmic inclusions, were obtained by segmentation; grey levels were standardized; and grey level histograms were generated. The histograms in follicular carcinoma showed Gaussian or near-Gaussian distribution and had a single peak, whereas those in papillary carcinoma had two peaks(bimodal), one at the black zone and the other at the white zone. In papillary carcinoma. the peak in the black zone represented an increased amount of heterochromatin particles and that at the white zone represented decreased electron density of euchromatin or nuclear matrix. These results indicate that the nuclei of follicular and papillary carcinoma cells differ intheir chromatin pattern and the difference may be due to decondensed chromatin and/or matrix substances.
Analysis of DNA Ploidy Patterns and Nuclear Morphometry in Diethylnitrosamine Induced Hepatocyte Nodules and Hepatocellular Carcinoma of Rats.
Chan Choi, Myung Kwan Kim, Kwan Mook Chae, Eun Cheol Kim, Hyung Bae Moon
Korean J Pathol. 1993;27(3):226-234.
  • 1,771 View
  • 18 Download
AbstractAbstract PDF
This study was designed to answer the question; (1) How does the DNA ploidy pattern change in hepatocarcinogenesis? (2) How does the nuclear morphology change in hepatocarcinogenesis? Diethylnitrosamine(DEN) (16.5 mg per kg) was subcutaneously injected to female Sprague-Dawley rats(150~200g) by weekly interval for 30 weeks. DNA ploidy and parameters of nuclear morphology were measured by image analyser(IBAS 200, Kontron, FRG). The DNA ploidy pattern was divided into three basic patterns(diploid, polyploid, and aneuploid modes). In 8 cases of saline-injected control rats, the DNA histograms showed all polyploid pattern. Inhepatocyte nodules(hyperplastic nodules), DNA diploidy was the most frequent pattern, being followed by polyploid and aneuploid DNA patterns, contrast to hepatocelular carcinomas in which polyploid DNA pattern was most frequently noted being followed by diploid and aneuploid DNA pattern. Although the nuclei of hepatocytes in hepatocyte nodules and hepatocellular carcinomas were larger and more pleomorphic than those of normal hepatocytes, they were as same as those of normal hepatocytes in regard to nuclear hyperchromasia. DNA content, which was increased in hepatocarcinogenesis, was significantly related to the nuclear area.
An Image Analytical Study on the Structural Spectrum of Intestinal Metaplasia-Dysplasia-Carcinoma of the Stomach.
Sang Woo Juhng, Dong Ha Park, Ji Shin Lee, Kyu Hyuk Cho
Korean J Pathol. 1993;27(1):50-57.
  • 1,547 View
  • 12 Download
AbstractAbstract PDF
Intestinal metaplasia and dysplasia of the stomach have been stressed as precursors of gastric carcinoma of the intestinal type, although their preneoplastic nature is still debated. In this study, the cytomorphometric and cytokinetic spectra of the suggested preneoplastic and neoplastic lesions of the stomach were investigated. From the resected stomachs of early gastric carcinoma of intestinal type, areas of normal, intestinal metaplasia, dysplasia, and carcinoma were selected. They were immunostained for proliferating cell nuclear antigen, counterstained with propidium iodide, and various nuclear parameters were measured by image analysis. Normal and intestinal metaplastic mucosae differed by the localization of proliferation zone, but not by nuclear profile area, circular shape factor, and proliferation index. In dysplasia, proliferation zone covered large parts of the dysplastic area. Nuclear profile area and proliferation index were larger whereas circular shape factor was smaller than in normal or intestinal metaplasia. Carcinomatous lesion had diffuse proliferation activity, the largest nuclear profile area and proliferating index, and circular shape factor in-between those of normal or intestinal metaplasia and dysplasia. The above results showed a structural spectrum among normal of intestinal metaplasia, dysplasia, and carcinoma of intestinal type in cytomorphometric and cytokinetic terms. The structural spectrum raises the possibility that dysplasia of the stomach is a preneoplastic lesion.
Evaluation of DNA Ploidy and Other Morphometric Parameters of Ovarian Mucinous Tumors.
Yun Mee Kim, Sang Woo Juhng, Joo Yong Yoo, Kyu Hyuk Cho
Korean J Pathol. 1991;25(5):397-406.
  • 1,459 View
  • 10 Download
AbstractAbstract
Biological behavior of malignant tumors has been assessed by morphological grading, clinical staging, and estimating other tumor markers. Recently DNA ploidy measured by flow cytometry and image analyser has been suggested as an additional useful indicator of the tumor behavior. In order to extract useful tumor cell-specific information in ovarian mucinous tumors, DNA contents and other morphologic parameters were measured by image analysis and DNA ploidy was also measured by flow cytometry. In all cases of cystadenoma, DNA diploidies were observed. In borderline malignancy, DNA diploidies were chiefly observed except one case of polyploidy. In true malignancy, DNA aneuploidies were observed except one case of polyploidy and two cases of diploidies by image analysis, and except four cases of diploides and one cas of polyploidy by flow cytometry. The statistical significance were observed in DNA ploidy pattern by image analysis. In nuclear areas, perimeters and major axis, statistical significance were not observed. These results suggest that DNA ploidy pattern are more or less independent parameter as an additional useful indicator of the histological grade of malignancy and that image analysis are better than flow cytometry in detecting DNA aneuploidy.
Morphometric Analysis of Cirrhotic Nodules in Hepatocellular Carcinoma-bearing Livers.
Gyeong Hoon Kang, Yong Il Kim
Korean J Pathol. 1991;25(4):338-345.
  • 1,599 View
  • 12 Download
AbstractAbstract PDF
It has been well known that liver cirrhosis, regardless of its etiology, is an important predisposing factor in hepatocarcinogenesis. However, the type of cirrhosis in hepatocellular carcinoma(HCC)-bearing liver varies not only by geographic areas but also with the cirteria applied for morphological classification of cirrhosis. To elucidate the relationship between the nodule size of HCC-bearing cirrhotic liver and clinicopathologic features, we measured cirrhotic nodule areas of 49 surgically resected HCC cases using image analyzer. The morphological type of cirrhosis was predominantly macronodular(49%), and followed by mixed(37%) and micronodular(14%). Seventy percent of the cases showed seropositivity for HBsAg. The average area of cirrhotic nodules was significantly larger in HBsAg-positive cases(mean: 6.14 mm2) than that of HBsAg-negative cases(mean: 2.5 mm2)(p<0.05), and their size was bigger in cases with grossly expansile pattern of HCC than those cases with infiltrative ones(p<0.05). Based on the above findings, we assume that seropositivity of HBsAg may influence on the regenerative activity of cirrhotic nodules and also subsequent increase of risk for further development of HCC. The presence of cirrhohsis and nodule size seem to be the important contributing factors to determine the growing patterns of HCC.
Evaluation of DNA Ploidy of Bronchogenic Carcinomas by Image Analysis.
Soo Sung Kim, Jae Hyuck Lee, Sang Woo Jung, Joo Yong Yoo
Korean J Pathol. 1991;25(3):238-244.
  • 1,428 View
  • 10 Download
AbstractAbstract PDF
In order to extract useful tumor cell-specific information. DNA contents and other morphological parameters were measured by image analysis. Single cell preparation was made from archived paraffin blocks of 14 cases of bronchogenic squamous cell carcinoma, poorly differentiated, by protease treatment. The cells were Feulgen stained, and DNA content, area, perimeter, and major axis of the tumor cell nuclei were measured. Inflammatory lymphocytes concurrent with the tumor cells were used as an internal standard. DNA ploidies of the lymphocytes and 2C tumor cells showed simple peaks with Gaussian distribution and mean coefficients of variation of 10% and 14% respectively. By the location and proportion of the tumor cells other than 2C cells, DNA ploidies could be classified into diploidy(1 case), polyploidy(2 cases), and aneuploidy(11 cases). The mean proportion of DNA aneuploidal tumor cells relative to the total tumor cells was 82.8%. In 8 cases, nuclear areas showed more or less overlapped distribution, whereas DNA contents showed discrete peaks. THes results suggest that many bronchogenic squamous cell carcinomas, poorly differentiated, have DNA aneuploidy and high proportion of aneuploidal cells, and that nuclear size and DNA content are more or less independent parameters.

J Pathol Transl Med : Journal of Pathology and Translational Medicine