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Volume 53(1); January 2019
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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
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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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  • 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
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    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
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    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
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    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
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    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
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    Modern Pathology.2021; 34(1): 4.     CrossRef
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    Coordination Chemistry Reviews.2021; 429: 213616.     CrossRef
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    Applied Sciences.2021; 11(2): 808.     CrossRef
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    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
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    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
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    Miao Cui, David Y. Zhang
    Laboratory Investigation.2021; 101(4): 412.     CrossRef
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    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
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    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
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    Journal of Physics: Conference Series.2021; 1767(1): 012031.     CrossRef
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    Diagnostics.2021; 11(3): 458.     CrossRef
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    The American Journal of Pathology.2021; 191(10): 1702.     CrossRef
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    BioMed Research International.2021; 2021: 1.     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
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    Scientific Reports.2021;[Epub]     CrossRef
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    Journal of Pathology Informatics.2021; 12(1): 45.     CrossRef
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    Acta Cytologica.2021; 65(4): 342.     CrossRef
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    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
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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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    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
Prognostic Role of S100A8 and S100A9 Protein Expressions in Non-small Cell Carcinoma of the Lung
Hyun Min Koh, Hyo Jung An, Gyung Hyuck Ko, Jeong Hee Lee, Jong Sil Lee, Dong Chul Kim, Jung Wook Yang, Min Hye Kim, Sung Hwan Kim, Kyung Nyeo Jeon, Gyeong-Won Lee, Se Min Jang, Dae Hyun Song
J Pathol Transl Med. 2019;53(1):13-22.   Published online November 26, 2018
DOI: https://doi.org/10.4132/jptm.2018.11.12
  • 6,634 View
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  • 12 Web of Science
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AbstractAbstract PDF
Background
S100A8 and S100A9 have been gaining recognition for modulating tumor growthand metastasis. This study aimed at evaluating the clinical significance of S100A8 and S100A9 innon-small cell lung cancer (NSCLC).
Methods
We analyzed the relationship between S100A8and S100A9 expressions, clinicopathological characteristics, and prognostic significance in tumorcells and peritumoral inflammatory cells.
Results
The positive staining of S100A8 in tumorcells was significantly increased in male (p < .001), smoker (p = .034), surgical method other thanlobectomy (p = .024), squamous cell carcinoma (SQCC) (p < .001) and higher TNM stage (p = .022)compared with female, non-smoker, lobectomy, adenocarcinoma (ADC), and lower stage. Theproportion of tumor cells stained for S100A8 was related to histologic type (p < .001) and patientsex (p = .027). The proportion of inflammatory cells stained for S100A8 was correlated with patientage (p = .022), whereas the proportion of inflammatory cells stained for S100A9 was correlatedwith patient sex (p < .001) and smoking history (p = .031). Moreover, positive staining in tumorcells, more than 50% of the tumor cells stained and less than 30% of the inflammatory cellsstained for S100A8 and S100A9 suggested a tendency towards increased survivability in SQCCbut towards decreased survivability in ADC.
Conclusions
S100A8 and S100A9 expressions might be potential prognostic markers in patients with NSCLC.

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  • Gene expression related to lung cancer altered by PHMG-p treatment in PBTE cells
    Yoon Hee Park, Sang Hoon Jeong, Hyejin Lee, Cherry Kim, Yoon Jeong Nam, Ja Young Kang, Jin Young Choi, Yu-Seon Lee, Su A. Park, Jaeyoung Kim, Eun-Kee Park, Yong-Wook Baek, Hong Lee, Ju-Han Lee
    Molecular & Cellular Toxicology.2023; 19(1): 205.     CrossRef
  • Discovery of protein biomarkers for venous thromboembolism in non-small cell lung cancer patients through data-independent acquisition mass spectrometry
    Yanhong Liu, Lan Gao, Yanru Fan, Rufei Ma, Yunxia An, Guanghui Chen, Yan Xie
    Frontiers in Oncology.2023;[Epub]     CrossRef
  • S100A8 and S100A9 in Cancer
    Yu Chen, Yuzhen Ouyang, Zhixin Li, Xiufang Wang, Jian Ma
    Biochimica et Biophysica Acta (BBA) - Reviews on Cancer.2023; 1878(3): 188891.     CrossRef
  • Gene expression of S100a8/a9 predicts Staphylococcus aureus-induced septic arthritis in mice
    Meghshree Deshmukh, Santhilal Subhash, Zhicheng Hu, Majd Mohammad, Anders Jarneborn, Rille Pullerits, Tao Jin, Pradeep Kumar Kopparapu
    Frontiers in Microbiology.2023;[Epub]     CrossRef
  • Single-cell immunophenotyping revealed the association of CD4+ central and CD4+ effector memory T cells linking exacerbating chronic obstructive pulmonary disease and NSCLC
    Nikolett Gémes, József Á. Balog, Patrícia Neuperger, Erzsébet Schlegl, Imre Barta, János Fillinger, Balázs Antus, Ágnes Zvara, Zoltán Hegedűs, Zsolt Czimmerer, Máté Manczinger, Gergő Mihály Balogh, József Tóvári, László G. Puskás, Gábor J. Szebeni
    Frontiers in Immunology.2023;[Epub]     CrossRef
  • A Prognostic Gene Signature for Hepatocellular Carcinoma
    Rong Chen, Meng Zhao, Yanli An, Dongfang Liu, Qiusha Tang, Gaojun Teng
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • The S100 protein family in lung cancer
    Ting Wang, Ge Du, Dong Wang
    Clinica Chimica Acta.2021; 520: 67.     CrossRef
  • The associations of serum S100A9 with the severity and prognosis in patients with community-acquired pneumonia: a prospective cohort study
    Hong-Yan Liu, Hui-Xian Xiang, Ying Xiang, Zheng Xu, Chun-Mei Feng, Jun Fei, Lin Fu, Hui Zhao
    BMC Infectious Diseases.2021;[Epub]     CrossRef
  • Saliva proteomic analysis reveals possible biomarkers of renal cell carcinoma
    Xiao Li Zhang, Zheng Zhi Wu, Yun Xu, Ji Guo Wang, Yong Qiang Wang, Mei Qun Cao, Chang Hao Wang
    Open Chemistry.2020; 18(1): 918.     CrossRef
  • Prognostic Role of S100A8 in Human Solid Cancers: A Systematic Review and Validation
    An Huang, Wei Fan, Jiacui Liu, Ben Huang, Qingyuan Cheng, Ping Wang, Yiping Duan, Tiantian Ma, Liangyue Chen, Yanping Wang, Mingxia Yu
    Frontiers in Oncology.2020;[Epub]     CrossRef
PLAG1, SOX10, and Myb Expression in Benign and Malignant Salivary Gland Neoplasms
Ji Hyun Lee, Hye Ju Kang, Chong Woo Yoo, Weon Seo Park, Jun Sun Ryu, Yuh-Seog Jung, Sung Weon Choi, Joo Yong Park, Nayoung Han
J Pathol Transl Med. 2019;53(1):23-30.   Published online November 14, 2018
DOI: https://doi.org/10.4132/jptm.2018.10.12
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AbstractAbstract PDF
Background
Recent findings in molecular pathology suggest that genetic translocation and/oroverexpression of oncoproteins is important in salivary gland tumorigenesis and diagnosis. Weinvestigated PLAG1, SOX10, and Myb protein expression in various salivary gland neoplasm tissues.
Methods
A total of 113 cases of surgically resected salivary gland neoplasms at the NationalCancer Center from January 2007 to March 2017 were identified. Immunohistochemical stainingof PLAG1, SOX10, and Myb in tissue samples was performed using tissue microarrays.
Results
Among the 113 cases, 82 (72.6%) were benign and 31 (27.4%) were malignant. PLAG1 showednuclear staining and normal parotid gland was not stained. Among 48 cases of pleomorphicadenoma, 29 (60.4%) were positive for PLAG1. All other benign and malignant salivary glandneoplasms were PLAG1-negative. SOX10 showed nuclear staining. In normal salivary gland tissuesSOX10 was expressed in cells of acinus and intercalated ducts. In benign tumors, SOX10 expressionwas observed in all pleomorphic adenoma (48/48), and basal cell adenoma (3/3), but not inother benign tumors. SOX10 positivity was observed in nine of 31 (29.0%) malignant tumors.Myb showed nuclear staining but was not detected in normal parotid glands. Four of 31 (12.9%)malignant tumors showed Myb positivity: three adenoid cystic carcinomas (AdCC) and onemyoepithelial carcinoma with focal AdCC-like histology.
Conclusions
PLAG1 expression is specificto pleomorphic adenoma. SOX10 expression is helpful to rule out excretory duct origin tumor,but its diagnostic value is relatively low. Myb is useful for diagnosing AdCC when histology isunclear in the surgical specimen.

Citations

Citations to this article as recorded by  
  • Immunohistochemical Characterization of a Large Cohort of Triple Negative Breast Cancer
    Rachel Han, Sharon Nofech-Mozes, Dina Boles, Hannah Wu, Nikolina Curcin, Elzbieta Slodkowska
    International Journal of Surgical Pathology.2024; 32(2): 239.     CrossRef
  • Proceedings of the 2024 North American Society of Head and Neck Pathology Companion Meeting, Baltimore, MD, March 24, 2024: Navigating Ancillary Studies in Basaloid/Blue Salivary Tumors
    Kristine S. Wong
    Head and Neck Pathology.2024;[Epub]     CrossRef
  • The Challenge of “Monomorphic” Mucoepidermoid Carcinoma—Report of a Rare Case with Pure Spindle-Clear Cell Morphology
    Xinyi Qu, Edwin Jun Chen Chew, Sathiyamoorthy Selvarajan, Bingcheng Wu, Abbas Agaimy, Fredrik Petersson
    Head and Neck Pathology.2023; 17(3): 864.     CrossRef
  • SOX10
    Albert L Sy, Mai P Hoang
    Journal of Clinical Pathology.2023; 76(10): 649.     CrossRef
  • Activating Transcription Factor 1 (ATF1) Immunohistochemical Marker Distinguishes HCCC from MEC
    Wafaey Badawy, Asmaa S. Abdelfattah, Haneen A. Sallam
    Journal of Molecular Pathology.2023; 4(3): 178.     CrossRef
  • Rare case of pleomorphic adenoma presenting as peritonsilar tumor
    Anđelina Jovanović, Svetlana Valjarević, Milan Jovanović
    Medicinska istrazivanja.2023; 56(3): 95.     CrossRef
  • Pleomorphic Adenoma of a Minor Salivary Gland of the Hard Palate: A Case Report
    Ishank Panchal, Anil Wanjari
    Cureus.2023;[Epub]     CrossRef
  • Advanced Diagnostic Methods for Salivary Glands Diseases: A Narrative Review Study
    Malak Mohammed AlOsaimi, Abdulaziz Mohammed AlSubaheen, Taif Saleh Jameel, Rand Abdulrahman AlSalamah, Dalal Naseh AlAnzi, Norah Ameen AlOushan, Fahad Fadhel AlShammari, Cristalle Soman
    Clinical Cancer Investigation Journal.2023; 12(4): 19.     CrossRef
  • Clinical Significance of SOX10 Expression in Human Pathology
    Hisham F. Bahmad, Aran Thiravialingam, Karthik Sriganeshan, Jeffrey Gonzalez, Veronica Alvarez, Stephanie Ocejo, Alvaro R. Abreu, Rima Avellan, Alejandro H. Arzola, Sana Hachem, Robert Poppiti
    Current Issues in Molecular Biology.2023; 45(12): 10131.     CrossRef
  • NR4A3 Immunostain Is a Highly Sensitive and Specific Marker for Acinic Cell Carcinoma in Cytologic and Surgical Specimens
    Kartik Viswanathan, Shaham Beg, Bing He, Taotao Zhang, Richard Cantley, Daniel J Lubin, Qiuying Shi, Zahra Maleki, Saeed Asiry, Rema Rao, Nora Katabi, Masato Nakaguro, William C Faquin, Peter M Sadow, Momin T Siddiqui, Theresa Scognamiglio
    American Journal of Clinical Pathology.2022; 157(1): 98.     CrossRef
  • Recent Advances on Immunohistochemistry and Molecular Biology for the Diagnosis of Adnexal Sweat Gland Tumors
    Nicolas Macagno, Pierre Sohier, Thibault Kervarrec, Daniel Pissaloux, Marie-Laure Jullie, Bernard Cribier, Maxime Battistella
    Cancers.2022; 14(3): 476.     CrossRef
  • Diagnostic accuracy of human transcriptional activator (Myb) expression by ELISA technique versus immunohistochemistry in detecting salivary gland carcinomas
    Yousra Refaey, OlfatGamil Shaker, Ayman Abdelwahab, ImanAdel Mohamed Abdelmoneim, Fat’heyaMohamed Zahran
    Journal of International Oral Health.2022; 14(1): 61.     CrossRef
  • SLUG is a key regulator of epithelial-mesenchymal transition in pleomorphic adenoma
    Hyesung Kim, Seung Bum Lee, Jae Kyung Myung, Jeong Hwan Park, Eunsun Park, Dong Il Kim, Cheol Lee, Younghoon Kim, Chul-Min Park, Min Bum Kim, Gil Chai Lim, Bogun Jang
    Laboratory Investigation.2022; 102(6): 631.     CrossRef
  • Assessment of MEF2C as a novel myoepithelial marker using normal salivary gland and pleomorphic adenoma: An immunohistochemical study
    Ikuko Takakura, Satoko Kujiraoka, Rika Yasuhara, Junichi Tanaka, Fumio Ide, Kenji Mishima
    Journal of Oral and Maxillofacial Surgery, Medicine, and Pathology.2022; 34(4): 523.     CrossRef
  • Update on selective special types of breast neoplasms: Focusing on controversies, differential diagnosis, and molecular genetic advances
    Shi Wei
    Seminars in Diagnostic Pathology.2022; 39(5): 367.     CrossRef
  • Cutaneous Melanomas: A Single Center Experience on the Usage of Immunohistochemistry Applied for the Diagnosis
    Costantino Ricci, Emi Dika, Francesca Ambrosi, Martina Lambertini, Giulia Veronesi, Corti Barbara
    International Journal of Molecular Sciences.2022; 23(11): 5911.     CrossRef
  • Distinct clinicopathological and genomic features in solid and basaloid adenoid cystic carcinoma of the breast
    Juan Ji, Fang Zhang, Fanglei Duan, Hong Yang, Jun Hou, Yang Liu, Jie Dai, Qiong Liao, Xian Chen, Qingsong Liu
    Scientific Reports.2022;[Epub]     CrossRef
  • NR4A3 fluorescence in situ hybridization analysis in cytologic and surgical specimens of acinic cell carcinoma
    Qiuying Shi, Bin Zhang, Caroline Bsirini, Liqiong Li, Ellen J. Giampoli, Kelly R. Magliocca, Michelle Reid, Zhongren Zhou
    Human Pathology.2022; 127: 86.     CrossRef
  • Evaluation of NR4A3 immunohistochemistry (IHC) and fluorescence in situ hybridization and comparison with DOG1 IHC for FNA diagnosis of acinic cell carcinoma
    John M. Skaugen, Raja R. Seethala, Simion I. Chiosea, Michael S. Landau
    Cancer Cytopathology.2021; 129(2): 104.     CrossRef
  • MYB-NFIB Translocation by FISH in Adenoid Cystic Carcinoma of the Head and Neck in Nigerian Patients: A Preliminary Report
    Adepitan A. Owosho, Olufunlola M. Adesina, Oluwole Odujoko, Olujide O. Soyele, Akinwumi Komolafe, Robert Bauer, Kallie Holte, Kurt F. Summersgill
    Head and Neck Pathology.2021; 15(2): 433.     CrossRef
  • Liquid-based cytology of oral brushings in a case of adenoid cystic carcinoma arising from the palate
    Ryo MAKINO, Akihiko KAWAHARA, Hideyuki ABE, Yorihiko TAKASE, Chihiro FUKUMITSU, Kazuya MURATA, Tomoko YOSHIDA, Yukako SHINODA, Yoshiki NAITO, Jun AKIBA
    The Journal of the Japanese Society of Clinical Cytology.2021; 60(1): 33.     CrossRef
  • MYB Translocations in Both Myoepithelial and Ductoglandular Epithelial Cells in Adenoid Cystic Carcinoma: A Histopathologic and Genetic Reappraisal in Six Primary Cutaneous Cases
    Keisuke Goto, Kazuyoshi Kajimoto, Takashi Sugino, Shin-ichi Nakatsuka, Makoto Yoshida, Mai Noto, Michihiro Kono, Toshihiro Takai
    The American Journal of Dermatopathology.2021; 43(4): 278.     CrossRef
  • Co-expression of Myoepithelial and Melanocytic Features in Carcinoma Ex Pleomorphic Adenoma
    Costantino Ricci, Federico Chiarucci, Francesca Ambrosi, Tiziana Balbi, Barbara Corti, Ottavio Piccin, Ernesto Pasquini, Maria Pia Foschini
    Head and Neck Pathology.2021; 15(4): 1385.     CrossRef
  • Juvenile onset pleomorphic adenoma presenting as giant tumor of parotid gland in a young female
    Surender Verma, Shivika Aggarwal, Pradeep Garg, Anjali Verma, Mridul Gera, SwaranS Yadav
    Journal of Dr. NTR University of Health Sciences.2021; 10(4): 286.     CrossRef
  • Cytopathology and diagnostics of Warthin's tumour
    Mirna Sučić, Nives Ljubić, Leila Perković, Dunja Ivanović, Leo Pažanin, Tena Sučić Radovanović, Dubravka Župnić‐Krmek, Fabijan Knežević
    Cytopathology.2020; 31(3): 193.     CrossRef
  • Clear cell papillary neoplasm of the breast with MAML2 gene rearrangement: Clear cell hidradenoma or low-grade mucoepidermoid carcinoma?
    Raima A. Memon, Carlos N Prieto Granada, Shi Wei
    Pathology - Research and Practice.2020; 216(10): 153140.     CrossRef
Uterine Malignant Mixed Müllerian Tumors Following Treatment with Selective Estrogen Receptor Modulators in Patients with Breast Cancer: A Report of 13 Cases and Their Clinicopathologic Characteristics
Byung-Kwan Jeong, Chang O. Sung, Kyu-Rae Kim
J Pathol Transl Med. 2019;53(1):31-39.   Published online December 18, 2018
DOI: https://doi.org/10.4132/jptm.2018.11.16
  • 5,886 View
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AbstractAbstract PDF
Background
Breast cancer treatment with selective estrogen receptor modulators (SERMs) increasesthe incidence of uterine malignant mixed Müllerian tumors (uMMMTs). We examine clinicopathologiccharacteristics and prognosis of SERM-associated uMMMTs (S-uMMMTs) and discusspossible pathogenetic mechanisms.
Methods
Among 28,104 patients with breast cancer, clinicopathologicfeatures and incidence of uMMMT were compared between patients who underwentSERM treatment and those who did not. Of 92 uMMMT cases that occurred during the same period,incidence, dose, and duration of SERM treatment, as well as overall survival rate, were comparedfor patients with breast cancer who underwent SERM treatment and those who did not (S-uMMMTvs NS-uMMMT) and for patients without breast cancer (de novo-uMMMT). Histopathologicalfindings and immunophenotypes for myogenin, desmin, p53, WT-1, estrogen receptor (ER) α, ERβ,progesterone receptor, and GATA-3 were compared between S-uMMMT and de novo-uMMMT.
Results
The incidence of S-uMMMT was significantly higher than that of NS-uMMMT (6.35-fold).All patients with SERM were postmenopausal and received daily 20–40 mg SERM. CumulativeSERM dose ranged from 21.9 to 73.0 g (mean, 46.0) over 39–192 months (mean, 107). Clinicopathologicfeatures, such as International Federation of Gynecology and Obstetrics stage andoverall survival, were not significantly different between patients with S-uMMMT and NS-uMMMTor between patients with S-uMMMT and de novo-uMMMT. All 11 S-uMMMT cases available forimmunostaining exhibited strong overexpression/null expression of p53 protein and significantlyincreased ERβ expression in carcinomatous and sarcomatous components.
Conclusions
SERMtherapy seemingly increases risk of S-uMMMT development; however, clinicopathologic featureswere similar in all uMMMTs from different backgrounds. p53 mutation and increased ERβ expressionmight be involved in the etiology of S-uMMMT.

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  • Tamoxifen/toremifene

    Reactions Weekly.2019; 1758(1): 330.     CrossRef
  • Molecular Basis of Tumor Heterogeneity in Endometrial Carcinosarcoma
    Leskela, Pérez-Mies, Rosa-Rosa, Cristobal, Biscuola, Palacios-Berraquero, Ong, Guia, Palacios
    Cancers.2019; 11(7): 964.     CrossRef
Prognostic Impact of Fusobacterium nucleatum Depends on Combined Tumor Location and Microsatellite Instability Status in Stage II/III Colorectal Cancers Treated with Adjuvant Chemotherapy
Hyeon Jeong Oh, Jung Ho Kim, Jeong Mo Bae, Hyun Jung Kim, Nam-Yun Cho, Gyeong Hoon Kang
J Pathol Transl Med. 2019;53(1):40-49.   Published online December 26, 2018
DOI: https://doi.org/10.4132/jptm.2018.11.29
  • 15,666 View
  • 236 Download
  • 33 Web of Science
  • 36 Crossref
AbstractAbstract PDFSupplementary Material
Background
This study aimed to investigate the prognostic impact of intratumoral Fusobacterium nucleatum in colorectal cancer (CRC) treated with adjuvant chemotherapy.
Methods
F. nucleatumDNA was quantitatively measured in a total of 593 CRC tissues retrospectively collectedfrom surgically resected specimens of stage III or high-risk stage II CRC patients who had receivedcurative surgery and subsequent oxaliplatin-based adjuvant chemotherapy (either FOLFOXor CAPOX). Each case was classified into one of the three categories: F. nucleatum–high, –low, or –negative.
Results
No significant differences in survival were observed between the F.nucleatum–high and –low/negative groups in the 593 CRCs (p = .671). Subgroup analyses accordingto tumor location demonstrated that disease-free survival was significantly better in F.nucleatum–high than in –low/negative patients with non-sigmoid colon cancer (including cecal,ascending, transverse, and descending colon cancers; n = 219; log-rank p = .026). In multivariateanalysis, F. nucleatum was determined to be an independent prognostic factor in non-sigmoidcolon cancers (hazard ratio, 0.42; 95% confidence interval, 0.18 to 0.97; p = .043). Furthermore,the favorable prognostic effect of F. nucleatum–high was observed only in a non-microsatellite instability-high (non-MSI-high) subset of non-sigmoid colon cancers (log-rank p = 0.014), but not ina MSI-high subset (log-rank p = 0.844), suggesting that the combined status of tumor locationand MSI may be a critical factor for different prognostic impacts of F. nucleatum in CRCs treatedwith adjuvant chemotherapy.
Conclusions
Intratumoral F. nucleatum load is a potential prognosticfactor in a non-MSI-high/non-sigmoid/non-rectal cancer subset of stage II/III CRCs treatedwith oxaliplatin-based adjuvant chemotherapy.

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  • Composition of the colon microbiota in the individuals with inflammatory bowel disease and colon cancer
    Ceren Acar, Sibel Kucukyildirim Celik, H. Ozgur Ozdemirel, Beril Erdem Tuncdemir, Saadet Alan, Hatice Mergen
    Folia Microbiologica.2024; 69(2): 333.     CrossRef
  • Intratumoral microorganisms in tumors of the digestive system
    Mengjuan Xuan, Xinyu Gu, Yingru Liu, Li Yang, Yi Li, Di Huang, Juan Li, Chen Xue
    Cell Communication and Signaling.2024;[Epub]     CrossRef
  • Prognostic impact of oral microbiome on survival of malignancies: a systematic review and meta-analysis
    Shuluan Li, Tianyu Wang, Ya Ren, Zhou Liu, Jidong Gao, Zhi Guo
    Systematic Reviews.2024;[Epub]     CrossRef
  • Exploring the Potential of Humoral Immune Response to Commensal Bifidobacterium as a Biomarker for Human Health, including Both Malignant and Non-Malignant Diseases: A Perspective on Detection Strategies and Future Directions
    Kyogo Itoh, Satoko Matsueda
    Biomedicines.2024; 12(4): 803.     CrossRef
  • Unveiling intratumoral microbiota: An emerging force for colorectal cancer diagnosis and therapy
    Jinjing Zhang, Penghui Wang, Jiafeng Wang, Xiaojie Wei, Mengchuan Wang
    Pharmacological Research.2024; 203: 107185.     CrossRef
  • Intratumoral microbiota: roles in cancer initiation, development and therapeutic efficacy
    Li Yang, Aitian Li, Ying Wang, Yi Zhang
    Signal Transduction and Targeted Therapy.2023;[Epub]     CrossRef
  • Increased Fusobacterium tumoural abundance affects immunogenicity in mucinous colorectal cancer and may be associated with improved clinical outcome
    William P. Duggan, Manuela Salvucci, Batuhan Kisakol, Andreas U. Lindner, Ian S. Reynolds, Heiko Dussmann, Joanna Fay, Tony O’Grady, Daniel B. Longley, Fiona Ginty, Elizabeth Mc Donough, Daniel J. Slade, John P. Burke, Jochen H. M. Prehn
    Journal of Molecular Medicine.2023; 101(7): 829.     CrossRef
  • Fusobacterium nucleatum Load Correlates with KRAS Mutation and Sessile Serrated Pathogenesis in Colorectal Adenocarcinoma
    Koki Takeda, Minoru Koi, Yoshiki Okita, Sija Sajibu, Temitope O. Keku, John M. Carethers
    Cancer Research Communications.2023; 3(9): 1940.     CrossRef
  • La asociación entre Fusobacterium nucleatum y el cáncer colorrectal: una revisión sistemática y metaanálisis
    Paola Villar-Ortega, Manuela Expósito-Ruiz, Miguel Gutiérrez-Soto, Miguel Ruiz-Cabello Jiménez, José María Navarro-Marí, José Gutiérrez-Fernández
    Enfermedades Infecciosas y Microbiología Clínica.2022; 40(5): 224.     CrossRef
  • The association between Fusobacterium nucleatum and cancer colorectal: A systematic review and meta-analysis
    Paola Villar-Ortega, Manuela Expósito-Ruiz, Miguel Gutiérrez-Soto, Miguel Ruiz-Cabello Jiménez, José María Navarro-Marí, José Gutiérrez-Fernández
    Enfermedades infecciosas y microbiologia clinica (English ed.).2022; 40(5): 224.     CrossRef
  • Suppression of Berberine and Probiotics (in vitro and in vivo) on the Growth of Colon Cancer With Modulation of Gut Microbiota and Butyrate Production
    Chao Huang, Ying Sun, Sheng-rong Liao, Zhao-xin Chen, Han-feng Lin, Wei-zeng Shen
    Frontiers in Microbiology.2022;[Epub]     CrossRef
  • Prognostic and clinicopathological significance of Fusobacterium nucleatum in colorectal cancer: a systemic review and meta-analysis
    Younghoon Kim, Nam Yun Cho, Gyeong Hoon Kang
    Journal of Pathology and Translational Medicine.2022; 56(3): 144.     CrossRef
  • Iron accelerates Fusobacterium nucleatum–induced CCL8 expression in macrophages and is associated with colorectal cancer progression
    Taishi Yamane, Yohei Kanamori, Hiroshi Sawayama, Hiromu Yano, Akihiro Nita, Yudai Ohta, Hironori Hinokuma, Ayato Maeda, Akiko Iwai, Takashi Matsumoto, Mayuko Shimoda, Mayumi Niimura, Shingo Usuki, Noriko Yasuda-Yoshihara, Masato Niwa, Yoshifumi Baba, Taka
    JCI Insight.2022;[Epub]     CrossRef
  • Clinicopathological differences of high Fusobacterium nucleatum levels in colorectal cancer: A review and meta-analysis
    Yi Wang, Yuting Wen, Jiayin Wang, Xin Lai, Ying Xu, Xuanping Zhang, Xiaoyan Zhu, Chenglin Ruan, Yao Huang
    Frontiers in Microbiology.2022;[Epub]     CrossRef
  • Clinical Significance of Fusobacterium nucleatum and Microsatellite Instability in Evaluating Colorectal Cancer Prognosis
    Yanxuan Xie, Xiaoyang Jiao, Mi Zeng, Zhiqiang Fan, Xin Li, Yumeng Yuan, Qiaoxin Zhang, Yong Xia
    Cancer Management and Research.2022; Volume 14: 3021.     CrossRef
  • Influence of the Microbiome Metagenomics and Epigenomics on Gastric Cancer
    Precious Mathebela, Botle Precious Damane, Thanyani Victor Mulaudzi, Zilungile Lynette Mkhize-Khwitshana, Guy Roger Gaudji, Zodwa Dlamini
    International Journal of Molecular Sciences.2022; 23(22): 13750.     CrossRef
  • Circulating IgA Antibodies Against Fusobacterium nucleatum Amyloid Adhesin FadA are a Potential Biomarker for Colorectal Neoplasia
    Jung Eun Baik, Li Li, Manish A. Shah, Daniel E. Freedberg, Zhezhen Jin, Timothy C. Wang, Yiping W. Han
    Cancer Research Communications.2022; 2(11): 1497.     CrossRef
  • Differential immune microenvironmental features of microsatellite-unstable colorectal cancers according to Fusobacterium nucleatum status
    Ji Ae Lee, Seung-Yeon Yoo, Hyeon Jeong Oh, Seorin Jeong, Nam-Yun Cho, Gyeong Hoon Kang, Jung Ho Kim
    Cancer Immunology, Immunotherapy.2021; 70(1): 47.     CrossRef
  • Fusobacterium nucleatum and Clinicopathologic Features of Colorectal Cancer: Results From the ColoCare Study
    Yannick Eisele, Patrick M. Mallea, Biljana Gigic, W. Zac Stephens, Christy A. Warby, Kate Buhrke, Tengda Lin, Juergen Boehm, Petra Schrotz-King, Sheetal Hardikar, Lyen C. Huang, T. Bartley Pickron, Courtney L. Scaife, Richard Viskochil, Torsten Koelsch, A
    Clinical Colorectal Cancer.2021; 20(3): e165.     CrossRef
  • Role of gut microbiota in epigenetic regulation of colorectal Cancer
    Yinghui Zhao, Chuanxin Wang, Ajay Goel
    Biochimica et Biophysica Acta (BBA) - Reviews on Cancer.2021; 1875(1): 188490.     CrossRef
  • Fusobacterium nucleatum: caution with interpreting historical patient sample cohort
    Kate L. F. Johnstone, Sinead Toomey, Stephen Madden, Brian D. P. O’Neill, Bryan T Hennessy
    Journal of Pathology and Translational Medicine.2021; 55(6): 415.     CrossRef
  • Fusobacterium nucleatum colonization is associated with decreased survival of helicobacter pylori-positive gastric cancer patients
    Yung-Yu Hsieh, Shui-Yi Tung, Hung-Yu Pan, Te-Sheng Chang, Kuo-Liang Wei, Wei-Ming Chen, Yi-Fang Deng, Chung-Kuang Lu, Yu-Hsuan Lai, Cheng-Shyong Wu, Chin Li
    World Journal of Gastroenterology.2021; 27(42): 7311.     CrossRef
  • Analysis of changes in microbiome compositions related to the prognosis of colorectal cancer patients based on tissue-derived 16S rRNA sequences
    Sukjung Choi, Jongsuk Chung, Mi-La Cho, Donghyun Park, Sun Shim Choi
    Journal of Translational Medicine.2021;[Epub]     CrossRef
  • Gastrointestinal tumors and infectious agents: A wide field to explore
    Miriam López-Gómez, Belén García de Santiago, Pedro-David Delgado-López, Eduardo Malmierca, Jesús González-Olmedo, César Gómez-Raposo, Carmen Sandoval, Pilar Ruiz-Seco, Nora Escribano, Jorge Francisco Gómez-Cerezo, Enrique Casado
    World Journal of Meta-Analysis.2021; 9(6): 505.     CrossRef
  • Gut Microbiota Profiles in Early- and Late-Onset Colorectal Cancer: A Potential Diagnostic Biomarker in the Future
    Murdani Abdullah, Ninik Sukartini, Saskia Aziza Nursyirwan, Rabbinu Rangga Pribadi, Hasan Maulahela, Amanda Pitarini Utari, Virly Nanda Muzellina, Agustinus Wiraatmadja, Kaka Renaldi
    Digestion.2021; 102(6): 823.     CrossRef
  • The effect of periodontal bacteria infection on incidence and prognosis of cancer
    Li Xiao, Qianyu Zhang, Yanshuang Peng, Daqing Wang, Ying Liu
    Medicine.2020; 99(15): e19698.     CrossRef
  • The impact of the gut microbiota on prognosis after surgery for colorectal cancer – a systematic review and meta‐analysis
    Emilie Palmgren Colov, Thea Helene Degett, Hans Raskov, Ismail Gögenur
    APMIS.2020; 128(2): 162.     CrossRef
  • Can the microbiota predict response to systemic cancer therapy, surgical outcomes, and survival? The answer is in the gut
    Khalid El Bairi, Rachid Jabi, Dario Trapani, Hanae Boutallaka, Bouchra Ouled Amar Bencheikh, Mohammed Bouziane, Mariam Amrani, Said Afqir, Adil Maleb
    Expert Review of Clinical Pharmacology.2020; 13(4): 403.     CrossRef
  • Predictive values of colon microbiota in the treatment response to colorectal cancer
    Jorge Galan-Ros, Verónica Ramos-Arenas, Pablo Conesa-Zamora
    Pharmacogenomics.2020; 21(14): 1045.     CrossRef
  • The gut microbiome and potential implications for early-onset colorectal cancer
    Reetu Mukherji, Benjamin A Weinberg
    Colorectal Cancer.2020;[Epub]     CrossRef
  • Fusobacterium nucleatum in the Colorectum and Its Association with Cancer Risk and Survival: A Systematic Review and Meta-analysis
    Christian Gethings-Behncke, Helen G. Coleman, Haydee W.T. Jordao, Daniel B. Longley, Nyree Crawford, Liam J. Murray, Andrew T. Kunzmann
    Cancer Epidemiology, Biomarkers & Prevention.2020; 29(3): 539.     CrossRef
  • CpG Island Methylation in Sessile Serrated Adenoma/Polyp of the Colorectum: Implications for Differential Diagnosis of Molecularly High-Risk Lesions among Non-dysplastic Sessile Serrated Adenomas/Polyps
    Ji Ae Lee, Hye Eun Park, Seung-Yeon Yoo, Seorin Jeong, Nam-Yun Cho, Gyeong Hoon Kang, Jung Ho Kim
    Journal of Pathology and Translational Medicine.2019; 53(4): 225.     CrossRef
  • Fusobacterium nucleatum tumor DNA levels are associated with survival in colorectal cancer patients
    Andrew T. Kunzmann, Marcela Alcântara Proença, Haydee WT Jordao, Katerina Jiraskova, Michaela Schneiderova, Miroslav Levy, Václav Liska, Tomas Buchler, Ludmila Vodickova, Veronika Vymetalkova, Ana Elizabete Silva, Pavel Vodicka, David J. Hughes
    European Journal of Clinical Microbiology & Infectious Diseases.2019; 38(10): 1891.     CrossRef
  • Gut Microbiome: A Promising Biomarker for Immunotherapy in Colorectal Cancer
    Sally Temraz, Farah Nassar, Rihab Nasr, Maya Charafeddine, Deborah Mukherji, Ali Shamseddine
    International Journal of Molecular Sciences.2019; 20(17): 4155.     CrossRef
  • The Four Horsemen in Colon Cancer
    Marco Antonio Hernández-Luna, Sergio López-Briones, Rosendo Luria-Pérez
    Journal of Oncology.2019; 2019: 1.     CrossRef
  • The role of Fusobacterium nucleatum in colorectal cancer: from carcinogenesis to clinical management
    Chun‐Hui Sun, Bin‐Bin Li, Bo Wang, Jing Zhao, Xiao‐Ying Zhang, Ting‐Ting Li, Wen‐Bing Li, Di Tang, Miao‐Juan Qiu, Xin‐Cheng Wang, Cheng‐Ming Zhu, Zhi‐Rong Qian
    Chronic Diseases and Translational Medicine.2019; 5(3): 178.     CrossRef
Quilty Lesions in the Endomyocardial Biopsies after Heart Transplantation
Haeyon Cho, Jin-Oh Choi, Eun-Seok Jeon, Jung-Sun Kim
J Pathol Transl Med. 2019;53(1):50-56.   Published online December 26, 2018
DOI: https://doi.org/10.4132/jptm.2018.11.30
  • 5,849 View
  • 117 Download
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AbstractAbstract PDFSupplementary Material
Background
The aim of this study was to investigate the clinical significance of Quilty lesions in endomyocardial biopsies (EMBs) of cardiac transplantation patients.
Methods
A total of 1190EMBs from 117 cardiac transplantation patients were evaluated histologically for Quilty lesions,acute cellular rejection, and antibody-mediated rejection. Cardiac allograft vasculopathy wasdiagnosed by computed tomography coronary angiography. Clinical information, including thepatients’ survival was retrieved by a review of medical records.
Results
Eighty-eight patients(75.2%) were diagnosed with Quilty lesions, which were significantly associated with acute cellularrejection, but not with acute cellular rejection ≥ 2R or antibody-mediated rejection. In patientsdiagnosed with both Quilty lesions and acute cellular rejection, the time-to-onset of Quilty lesionsfrom transplantation was longer than that of acute cellular rejections. We found a significant associationbetween Quilty lesions and cardiac allograft vasculopathy. No significant relationship wasfound between Quilty lesions and the patients’ survival.
Conclusions
Quilty lesion may be an indicator of previous acute cellular rejection rather than a predictor for future acute cellular rejection.

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    Kevin C. Bermea, Nicolas Kostelecky, Sylvie T. Rousseau, Chieh-Yu Lin, Luigi Adamo
    Frontiers in Immunology.2022;[Epub]     CrossRef
  • Examination of tracheal allografts after long-term survival in dogs
    Tao Lu, Yiwei Huang, Yulei Qiao, Yongxing Zhang, Yu Liu
    European Journal of Cardio-Thoracic Surgery.2021; 59(1): 155.     CrossRef
  • Essentials in the diagnosis of postoperative myocardial lesions similar to or unrelated to rejection in heart transplant
    Costel Dumitru, Ancuta Zazgyva, Adriana Habor, Ovidiu Cotoi, Horațiu Suciu, Carmen Cotrutz, Bogdan Grecu, Ileana Anca Sin
    Revista Romana de Medicina de Laborator.2021; 29(3): 307.     CrossRef
  • Clinical outcome of donor heart with prolonged cold ischemic time: A single‐center study
    Fazal Shafiq, Yixuan Wang, Geng Li, Zongtao Liu, Fei Li, Ying Zhou, Li Xu, Xingjian Hu, Nianguo Dong
    Journal of Cardiac Surgery.2020; 35(2): 397.     CrossRef
  • The XVth Banff Conference on Allograft Pathology the Banff Workshop Heart Report: Improving the diagnostic yield from endomyocardial biopsies and Quilty effect revisited
    Jean-Paul Duong Van Huyen, Marny Fedrigo, Gregory A. Fishbein, Ornella Leone, Desley Neil, Charles Marboe, Eliot Peyster, Jan von der Thüsen, Alexandre Loupy, Michael Mengel, Monica P. Revelo, Benjamin Adam, Patrick Bruneval, Annalisa Angelini, Dylan V. M
    American Journal of Transplantation.2020; 20(12): 3308.     CrossRef
Case Studies
Primary Peripheral Gamma Delta T-Cell Lymphoma of the Central Nervous System: Report of a Case Involving the Intramedullary Spinal Cord and Presenting with Myelopathy
Jeemin Yim, Seung Geun Song, Sehui Kim, Jae Won Choi, Kyu-Chong Lee, Jeong Mo Bae, Yoon Kyung Jeon
J Pathol Transl Med. 2019;53(1):57-61.   Published online October 1, 2018
DOI: https://doi.org/10.4132/jptm.2018.08.21
  • 5,030 View
  • 151 Download
  • 5 Web of Science
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AbstractAbstract PDF
Primary central nervous system lymphoma of T-cell origin (T-PCNSL) is rare, and its clinicopathological features remain unclear. Peripheral T-cell lymphoma of γδ T-cell origin is an aggressive lymphoma mainly involving extranodal sites. Here, we report a case of γδ T-PCNSL involving the intramedullary spinal cord and presenting with paraplegia. A 75-year-old Korean woman visited the hospital complaining of back pain and lower extremity weakness. Magnetic resonance imaging revealed multifocal enhancing intramedullary nodular lesions in the thoracic and lumbar spinal cord. An enhancing nodular lesion was observed in the periventricular white matter of the lateral ventricle in the brain. There were no other abnormalities in systemic organs or skin. Laminectomy and tumor removal were performed. The tumor consisted of monomorphic, medium-to-large atypical lymphocytes with pale-to-eosinophilic cytoplasm. Immunohistochemically, the tumor cells were CD3(+), TCRβF1(-), TCRγ(+), CD30(-), CD4(-), CD8(-), CD56(+), TIA1(+), granzyme B(+), and CD103(+). Epstein-Barr virus in situ was negative. This case represents a unique T-PCNSL of γδ T-cell origin involving the spinal cord.

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  • B-Cell Lymphoma Intramedullary Tumor: Case Report and Systematic Review
    Daniel Gregório Gonsalves, Paulo Eduardo Albuquerque Zito Raffa, Gabriela Gerenutti de Sousa, Melissa Esposito Gomes Rigueiral, Iracema Araújo Estevão, Cesar Cozar Pacheco, Roger Thomaz Rotta Medeiros, Paulo Roberto Franceschini, Paulo Henrique Pires de A
    Asian Journal of Neurosurgery.2023; 18(02): 231.     CrossRef
  • Primary intramedullary spinal cord lymphoma misdiagnosed as longitudinally extensive transverse myelitis: a case report and literature review
    Huizhen Ge, Li Xu, Huajie Gao, Suqiong Ji
    BMC Neurology.2023;[Epub]     CrossRef
  • Clinicopathologic and Genetic Features of Primary T-cell Lymphomas of the Central Nervous System
    Jeemin Yim, Jiwon Koh, Sehui Kim, Seung Geun Song, Jeong Mo Bae, Hongseok Yun, Ji-Youn Sung, Tae Min Kim, Sung-Hye Park, Yoon Kyung Jeon
    American Journal of Surgical Pathology.2022; 46(4): 486.     CrossRef
  • Peripheral T-Cell Lymphomas Involving the Central Nervous System: A Report From the Czech Lymphoma Study Group Registry
    Heidi Mocikova, Robert Pytlík, Katerina Benesova, Andrea Janikova, Juraj Duras, Alice Sykorova, Katerina Steinerova, Vit Prochazka, Vit Campr, David Belada, Marek Trneny
    Frontiers in Oncology.2022;[Epub]     CrossRef
TFE3-Expressing Perivascular Epithelioid Cell Tumor of the Breast
Hyunjin Kim, Jimin Kim, Se Kyung Lee, Eun Yoon Cho, Soo Youn Cho
J Pathol Transl Med. 2019;53(1):62-65.   Published online October 1, 2018
DOI: https://doi.org/10.4132/jptm.2018.08.30
  • 6,558 View
  • 145 Download
  • 15 Web of Science
  • 9 Crossref
AbstractAbstract PDF
Perivascular epithelioid cell tumor (PEComa) is a very rare mesenchymal tumor with a distinctive morphology and immunophenotype. PEComas usually harbor TSC2 alterations, although TFE3 translocations, which occur in MiT family translocation renal cell carcinoma and alveolar soft part sarcoma, are also possible. We recently experienced a case of PEComa with TFE3 expression arising in the breast. An 18-year-old female patient presented with a right breast mass. Histologically, the tumor consisted of epithelioid cells with alveolar structure and showed a diffuse strong expression of HMB45 and TFE3. TSC2 was preserved. Melan A and smooth muscle actin were negative. To our knowledge, this is the first Korean case of PEComa of the breast that intriguingly presented with TFE3 expression.

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  • Malignant lung PEComa (clear cell tumor): rare case report and literature review
    Marcos Adriano Garcia Campos, Lucas Fernandes Vasques, Rafael Goulart de Medeiros, Érico Murilo Monteiro Cutrim, Ana Júlia Favarin, Sarah Rebecca Machado Silva, Gyl Eanes Barros Silva, Marcelo Padovani de Toledo Moraes, Mariana Lopes Zanatta, Diego Aparec
    Frontiers in Oncology.2023;[Epub]     CrossRef
  • Cathepsin K: A Versatile Potential Biomarker and Therapeutic Target for Various Cancers
    Die Qian, Lisha He, Qing Zhang, Wenqing Li, Dandan Tang, Chunjie Wu, Fei Yang, Ke Li, Hong Zhang
    Current Oncology.2022; 29(8): 5963.     CrossRef
  • Endometrioid Carcinomas of the Ovaries and Endometrium Involving Endocervical Polyps: Comprehensive Clinicopathological Analyses
    Jihee Sohn, Yurimi Lee, Hyun-Soo Kim
    Diagnostics.2022; 12(10): 2339.     CrossRef
  • Serous Carcinoma of the Endometrium with Mesonephric-Like Differentiation Initially Misdiagnosed as Uterine Mesonephric-Like Adenocarcinoma: A Case Report with Emphasis on the Immunostaining and the Identification of Splice Site TP53 Mutation
    Sangjoon Choi, Yoon Yang Jung, Hyun-Soo Kim
    Diagnostics.2021; 11(4): 717.     CrossRef
  • Mesonephric-like Differentiation of Endometrial Endometrioid Carcinoma: Clinicopathological and Molecular Characteristics Distinct from Those of Uterine Mesonephric-like Adenocarcinoma
    Sujin Park, Go Eun Bae, Jiyoung Kim, Hyun-Soo Kim
    Diagnostics.2021; 11(8): 1450.     CrossRef
  • Mesonephric-like Adenocarcinoma of the Uterine Corpus: Comprehensive Immunohistochemical Analyses Using Markers for Mesonephric, Endometrioid and Serous Tumors
    Hyunjin Kim, Kiyong Na, Go Eun Bae, Hyun-Soo Kim
    Diagnostics.2021; 11(11): 2042.     CrossRef
  • Invasive Lobular Carcinoma With Extensive Clear Cells: A Pitfall in Diagnosis
    Mark H. Kavesh, Daniel Sanchez, Jaya Ruth Asirvatham
    International Journal of Surgical Pathology.2020; 28(2): 169.     CrossRef
  • Glycogen-rich Clear Cell Carcinoma of the Breast: A Comprehensive Review
    Semir Vranic, Faruk Skenderi, Vanesa Beslagic, Zoran Gatalica
    Applied Immunohistochemistry & Molecular Morphology.2020; 28(9): 655.     CrossRef
  • TFE3-expressing primary perivascular epithelioid cell tumor of the Lymph node mimicking nodal relapse of rectal cancer: A case report
    Jongmin Park, An Na Seo
    International Journal of Surgery Case Reports.2019; 59: 46.     CrossRef
Brief Case Report
Rare Manifestations of Churg-Strauss Syndrome with Mediastinal and Hilar Lymphadenopathies: Report of an Autopsy Case
Woo Cheal Cho, Bharat Ramlal, Mary Fiel-Gan, Xianyuan Song
J Pathol Transl Med. 2019;53(1):66-69.   Published online December 18, 2017
DOI: https://doi.org/10.4132/jptm.2017.12.13
  • 6,446 View
  • 150 Download
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PDF

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  • Radiological significance of mediastinal lymphadenopathy in eosinophilic granulomatosis with polyangiitis
    Hisashi Sasaki, Jun Miyata, Ryohei Suematsu, Yoshifumi Kimizuka, Yuji Fujikura, Yoshiko Kichikawa, Hiroaki Sugiura, Kenji Itoh, Akihiko Kawana
    Allergology International.2022; 71(4): 536.     CrossRef
Case Study
Cytopathologic Features of Secretory Carcinoma of Salivary Gland: Report of Two Cases
Young Ah Kim, Jae Won Joung, Sun-Jae Lee, Hoon-Kyu Oh, Chang Ho Cho, Woo Jung Sung
J Pathol Transl Med. 2019;53(1):70-74.   Published online December 28, 2018
DOI: https://doi.org/10.4132/jptm.2018.11.09
  • 5,464 View
  • 130 Download
  • 6 Web of Science
  • 6 Crossref
AbstractAbstract PDF
Secretory carcinoma of the salivary gland (SC) is a newly introduced rare salivary gland tumor that shares histological, immunohistochemical, and genetic characteristics with secretory carcinoma of the breast. Here, we report the cytologic features of two cases of SC confirmed by surgical resection. In these two cases, SC was incidentally detected in a 64-year-old female and a 56-yearold male. Fine needle aspiration cytology revealed nests of tumor cells with a papillary or glandular structure floating in mucinous secretions. The tumor cells demonstrated uniform, round, smooth nuclear contours and distinct nucleoli. Multiple characteristic cytoplasmic vacuoles were revealed. Singly scattered tumor cells frequently showed variable sized cytoplasmic vacuoles. The cytopathologic diagnosis of SC should be considered when characteristic cytological findings are revealed. Further immunohistochemistry and gene analyses are helpful to diagnose SC.

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  • Salivary Gland Secretory Carcinoma; Review of 13 Years World‐Wide Experience and Meta‐Analysis
    Eyal Yosefof, Tomer Boldes, Daniel Dan, Eyal Robenshtok, Yulia Strenov, Gideon Bachar, Thomas Shpitzer, Aviram Mizrachi
    The Laryngoscope.2024; 134(4): 1716.     CrossRef
  • Efficacy of Fine-Needle Aspiration Cytology in Diagnosing Secretory Carcinoma of Salivary Gland: A Systematic Review and Meta-Analysis
    Pooja Sharma Kala, Mamta Gupta, Naveen Thapliyal
    Acta Cytologica.2024; : 1.     CrossRef
  • An Underappreciated Cytomorphological Feature of Secretory Carcinoma of Salivary Gland on Fine Needle Aspiration Biopsy: Case Report with Literature Review
    Yinan Hua, Bing Leng, Kenneth E. Youens, Lina Liu
    Head and Neck Pathology.2022; 16(2): 567.     CrossRef
  • Prognostic factors in mammary analogue secretory carcinomas of the parotid gland: Systematic review and meta‐analysis
    Stefan Janik, Muhammad Faisal, Blazen Marijić, Stefan Grasl, Matthaeus Ch. Grasl, Gregor Heiduschka, Boban M. Erovic
    Head & Neck.2022; 44(3): 792.     CrossRef
  • A systematic review of secretory carcinoma of the salivary gland: where are we?
    Lísia Daltro Borges Alves, Andreia Cristina de Melo, Thayana Alves Farinha, Luiz Henrique de Lima Araujo, Leandro de Souza Thiago, Fernando Luiz Dias, Héliton Spíndola Antunes, Ana Lucia Amaral Eisenberg, Luiz Claudio Santos Thuler, Daniel Cohen Goldember
    Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology.2021; 132(4): e143.     CrossRef
  • Clinical characteristics of acinic cell carcinoma and secretory carcinoma of the parotid gland
    Tetsuya Terada, Ryo Kawata, Keiki Noro, Masaaki Higashino, Shuji Nishikawa, Shin-ichi Haginomori, Yoshitaka Kurisu, Hiroko Kuwabara, Yoshinobu Hirose
    European Archives of Oto-Rhino-Laryngology.2019; 276(12): 3461.     CrossRef

J Pathol Transl Med : Journal of Pathology and Translational Medicine