Skip Navigation
Skip to contents

J Pathol Transl Med : Journal of Pathology and Translational Medicine

OPEN ACCESS
SEARCH
Search

Previous issues

Page Path
HOME > Articles and issues > Previous issues
11 Previous issues
Filter
Filter
Article category
Keywords
Authors
Funded articles
Volume 54(2); March 2020
Prev issue Next issue
Review
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
J Pathol Transl Med. 2020;54(2):125-134.   Published online February 13, 2020
DOI: https://doi.org/10.4132/jptm.2019.12.31
  • 14,280 View
  • 559 Download
  • 63 Web of Science
  • 64 Crossref
AbstractAbstract PDF
Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.

Citations

Citations to this article as recorded by  
  • Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
    Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J.V. Campbell, Andrew P. Norgan, Cynthia Lokker
    Journal of Pathology Informatics.2024; 15: 100348.     CrossRef
  • Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology
    Gisela Magalhães, Rita Calisto, Catarina Freire, Regina Silva, Diana Montezuma, Sule Canberk, Fernando Schmitt
    Journal of Histotechnology.2024; 47(1): 39.     CrossRef
  • PATrans: Pixel-Adaptive Transformer for edge segmentation of cervical nuclei on small-scale datasets
    Hexuan Hu, Jianyu Zhang, Tianjin Yang, Qiang Hu, Yufeng Yu, Qian Huang
    Computers in Biology and Medicine.2024; 168: 107823.     CrossRef
  • CNAC-Seg: Effective segmentation for cervical nuclei in adherent cells and clusters via exploring gaps of receptive fields
    Hexuan Hu, Jianyu Zhang, Tianjin Yang, Qiang Hu, Yufeng Yu, Qian Huang
    Biomedical Signal Processing and Control.2024; 90: 105833.     CrossRef
  • Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications
    Swati Satturwar, Anil V. Parwani
    Advances in Anatomic Pathology.2024; 31(2): 136.     CrossRef
  • Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer
    Jonghyun Lee, Seunghyun Cha, Jiwon Kim, Jung Joo Kim, Namkug Kim, Seong Gyu Jae Gal, Ju Han Kim, Jeong Hoon Lee, Yoo-Duk Choi, Sae-Ryung Kang, Ga-Young Song, Deok-Hwan Yang, Jae-Hyuk Lee, Kyung-Hwa Lee, Sangjeong Ahn, Kyoung Min Moon, Myung-Giun Noh
    Cancers.2024; 16(2): 430.     CrossRef
  • Artificial intelligence’s impact on breast cancer pathology: a literature review
    Amr Soliman, Zaibo Li, Anil V. Parwani
    Diagnostic Pathology.2024;[Epub]     CrossRef
  • Blockchain: A safe digital technology to share cancer diagnostic results in pandemic times—Challenges and legacy for the future
    Bruno Natan Santana Lima, Lucas Alves da Mota Santana, Rani Iani Costa Gonçalo, Carla Samily de Oliveira Costa, Daniel Pitanga de Sousa Nogueira, Cleverson Luciano Trento, Wilton Mitsunari Takeshita
    Oral Surgery.2023; 16(3): 300.     CrossRef
  • Pathologists’ acceptance of telepathology in the Ministry of National Guard Health Affairs Hospitals in Saudi Arabia: A survey study
    Raneem Alawashiz, Sharifah Abdullah AlDossary
    DIGITAL HEALTH.2023; 9: 205520762311636.     CrossRef
  • An Atrous Convolved Hybrid Seg-Net Model with residual and attention mechanism for gland detection and segmentation in histopathological images
    Manju Dabass, Jyoti Dabass
    Computers in Biology and Medicine.2023; 155: 106690.     CrossRef
  • Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms
    Jamshid Abdul-Ghafar, Kyung Jin Seo, Hye-Ra Jung, Gyeongsin Park, Seung-Sook Lee, Yosep Chong
    Diagnostics.2023; 13(7): 1308.     CrossRef
  • Diagnosing Infectious Diseases in Poultry Requires a Holistic Approach: A Review
    Dieter Liebhart, Ivana Bilic, Beatrice Grafl, Claudia Hess, Michael Hess
    Poultry.2023; 2(2): 252.     CrossRef
  • Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers
    Mohammad Rizwan Alam, Kyung Jin Seo, Jamshid Abdul-Ghafar, Kwangil Yim, Sung Hak Lee, Hyun-Jong Jang, Chan Kwon Jung, Yosep Chong
    Briefings in Bioinformatics.2023;[Epub]     CrossRef
  • Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis
    Giovanni P. Burrai, Andrea Gabrieli, Marta Polinas, Claudio Murgia, Maria Paola Becchere, Pierfranco Demontis, Elisabetta Antuofermo
    Animals.2023; 13(9): 1563.     CrossRef
  • Rapid digital pathology of H&E-stained fresh human brain specimens as an alternative to frozen biopsy
    Bhaskar Jyoti Borah, Yao-Chen Tseng, Kuo-Chuan Wang, Huan-Chih Wang, Hsin-Yi Huang, Koping Chang, Jhih Rong Lin, Yi-Hua Liao, Chi-Kuang Sun
    Communications Medicine.2023;[Epub]     CrossRef
  • Applied machine learning in hematopathology
    Taher Dehkharghanian, Youqing Mu, Hamid R. Tizhoosh, Clinton J. V. Campbell
    International Journal of Laboratory Hematology.2023; 45(S2): 87.     CrossRef
  • Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images
    Marco Fragoso-Garcia, Frauke Wilm, Christof A. Bertram, Sophie Merz, Anja Schmidt, Taryn Donovan, Andrea Fuchs-Baumgartinger, Alexander Bartel, Christian Marzahl, Laura Diehl, Chloe Puget, Andreas Maier, Marc Aubreville, Katharina Breininger, Robert Klopf
    Veterinary Pathology.2023; 60(6): 865.     CrossRef
  • Artificial Intelligence in the Pathology of Gastric Cancer
    Sangjoon Choi, Seokhwi Kim
    Journal of Gastric Cancer.2023; 23(3): 410.     CrossRef
  • Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy
    Ching-Wei Wang, Kai-Lin Chu, Hikam Muzakky, Yi-Jia Lin, Tai-Kuang Chao
    Cancers.2023; 15(15): 3991.     CrossRef
  • Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies
    Dennis H Murphree, Yong-hun Kim, Kirk A Sidey, Nneka I Comfere, Nahid Y Vidal
    Clinical and Experimental Dermatology.2023;[Epub]     CrossRef
  • Multi-Configuration Analysis of DenseNet Architecture for Whole Slide Image Scoring of ER-IHC
    Wan Siti Halimatul Munirah Wan Ahmad, Mohammad Faizal Ahmad Fauzi, Md Jahid Hasan, Zaka Ur Rehman, Jenny Tung Hiong Lee, See Yee Khor, Lai-Meng Looi, Fazly Salleh Abas, Afzan Adam, Elaine Wan Ling Chan, Sei-Ichiro Kamata
    IEEE Access.2023; 11: 79911.     CrossRef
  • Digitization of Pathology Labs: A Review of Lessons Learned
    Lars Ole Schwen, Tim-Rasmus Kiehl, Rita Carvalho, Norman Zerbe, André Homeyer
    Laboratory Investigation.2023; 103(11): 100244.     CrossRef
  • Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges
    Xianzheng Qin, Taojing Ran, Yifei Chen, Yao Zhang, Dong Wang, Chunhua Zhou, Duowu Zou
    Diagnostics.2023; 13(19): 3054.     CrossRef
  • Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer
    Hyun-Jong Jang, Jai-Hyang Go, Younghoon Kim, Sung Hak Lee
    Cancers.2023; 15(22): 5389.     CrossRef
  • Using digital pathology to analyze the murine cerebrovasculature
    Dana M Niedowicz, Jenna L Gollihue, Erica M Weekman, Panhavuth Phe, Donna M Wilcock, Christopher M Norris, Peter T Nelson
    Journal of Cerebral Blood Flow & Metabolism.2023;[Epub]     CrossRef
  • AIR-UNet++: a deep learning framework for histopathology image segmentation and detection
    Anusree Kanadath, J. Angel Arul Jothi, Siddhaling Urolagin
    Multimedia Tools and Applications.2023;[Epub]     CrossRef
  • Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
    Stephanie S. Noronha, Mayuri A. Mehta, Dweepna Garg, Ketan Kotecha, Ajith Abraham
    IEEE Access.2023; 11: 140348.     CrossRef
  • Digital pathology and artificial intelligence in translational medicine and clinical practice
    Vipul Baxi, Robin Edwards, Michael Montalto, Saurabh Saha
    Modern Pathology.2022; 35(1): 23.     CrossRef
  • Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models
    Valeria Bertani, Olivier Blanck, Davy Guignard, Frederic Schorsch, Hannah Pischon
    Toxicologic Pathology.2022; 50(1): 23.     CrossRef
  • Investigating the genealogy of the literature on digital pathology: a two-dimensional bibliometric approach
    Dayu Hu, Chengyuan Wang, Song Zheng, Xiaoyu Cui
    Scientometrics.2022; 127(2): 785.     CrossRef
  • Digital Dermatopathology and Its Application to Mohs Micrographic Surgery
    Yeongjoo Oh, Hye Min Kim, Soon Won Hong, Eunah Shin, Jihee Kim, Yoon Jung Choi
    Yonsei Medical Journal.2022; 63(Suppl): S112.     CrossRef
  • Assessment of parathyroid gland cellularity by digital slide analysis
    Rotem Sagiv, Bertha Delgado, Oleg Lavon, Vladislav Osipov, Re'em Sade, Sagi Shashar, Ksenia M. Yegodayev, Moshe Elkabets, Ben-Zion Joshua
    Annals of Diagnostic Pathology.2022; 58: 151907.     CrossRef
  • PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System
    Muhammad Nurmahir Mohamad Sehmi, Mohammad Faizal Ahmad Fauzi, Wan Siti Halimatul Munirah Wan Ahmad, Elaine Wan Ling Chan
    Frontiers in Signal Processing.2022;[Epub]     CrossRef
  • Classification of Mouse Lung Metastatic Tumor with Deep Learning
    Ha Neul Lee, Hong-Deok Seo, Eui-Myoung Kim, Beom Seok Han, Jin Seok Kang
    Biomolecules & Therapeutics.2022; 30(2): 179.     CrossRef
  • Techniques for digital histological morphometry of the pineal gland
    Bogdan-Alexandru Gheban, Horaţiu Alexandru Colosi, Ioana-Andreea Gheban-Roșca, Carmen Georgiu, Dan Gheban, Doiniţa Crişan, Maria Crişan
    Acta Histochemica.2022; 124(4): 151897.     CrossRef
  • Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape
    Muhammad Joan Ailia, Nishant Thakur, Jamshid Abdul-Ghafar, Chan Kwon Jung, Kwangil Yim, Yosep Chong
    Cancers.2022; 14(10): 2400.     CrossRef
  • Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review
    Mohammad Rizwan Alam, Jamshid Abdul-Ghafar, Kwangil Yim, Nishant Thakur, Sung Hak Lee, Hyun-Jong Jang, Chan Kwon Jung, Yosep Chong
    Cancers.2022; 14(11): 2590.     CrossRef
  • Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis
    Takayuki Takahashi, Hikaru Matsuoka, Rieko Sakurai, Jun Akatsuka, Yusuke Kobayashi, Masaru Nakamura, Takashi Iwata, Kouji Banno, Motomichi Matsuzaki, Jun Takayama, Daisuke Aoki, Yoichiro Yamamoto, Gen Tamiya
    Journal of Gynecologic Oncology.2022;[Epub]     CrossRef
  • Digital Pathology and Artificial Intelligence Applications in Pathology
    Heounjeong Go
    Brain Tumor Research and Treatment.2022; 10(2): 76.     CrossRef
  • Mass spectrometry imaging to explore molecular heterogeneity in cell culture
    Tanja Bien, Krischan Koerfer, Jan Schwenzfeier, Klaus Dreisewerd, Jens Soltwisch
    Proceedings of the National Academy of Sciences.2022;[Epub]     CrossRef
  • Integrating artificial intelligence in pathology: a qualitative interview study of users' experiences and expectations
    Jojanneke Drogt, Megan Milota, Shoko Vos, Annelien Bredenoord, Karin Jongsma
    Modern Pathology.2022; 35(11): 1540.     CrossRef
  • Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data
    Veronika Shavlokhova, Michael Vollmer, Patrick Gholam, Babak Saravi, Andreas Vollmer, Jürgen Hoffmann, Michael Engel, Christian Freudlsperger
    Journal of Personalized Medicine.2022; 12(9): 1471.     CrossRef
  • Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images
    JaeYen Song, Soyoung Im, Sung Hak Lee, Hyun-Jong Jang
    Diagnostics.2022; 12(11): 2623.     CrossRef
  • A self-supervised contrastive learning approach for whole slide image representation in digital pathology
    Parsa Ashrafi Fashi, Sobhan Hemati, Morteza Babaie, Ricardo Gonzalez, H.R. Tizhoosh
    Journal of Pathology Informatics.2022; 13: 100133.     CrossRef
  • A Matched-Pair Analysis of Nuclear Morphologic Features Between Core Needle Biopsy and Surgical Specimen in Thyroid Tumors Using a Deep Learning Model
    Faridul Haq, Andrey Bychkov, Chan Kwon Jung
    Endocrine Pathology.2022; 33(4): 472.     CrossRef
  • Development of quality assurance program for digital pathology by the Korean Society of Pathologists
    Yosep Chong, Jeong Mo Bae, Dong Wook Kang, Gwangil Kim, Hye Seung Han
    Journal of Pathology and Translational Medicine.2022; 56(6): 370.     CrossRef
  • Machine learning in renal pathology
    Matthew Nicholas Basso, Moumita Barua, Julien Meyer, Rohan John, April Khademi
    Frontiers in Nephrology.2022;[Epub]     CrossRef
  • Whole Slide Image Quality in Digital Pathology: Review and Perspectives
    Romain Brixtel, Sebastien Bougleux, Olivier Lezoray, Yann Caillot, Benoit Lemoine, Mathieu Fontaine, Dalal Nebati, Arnaud Renouf
    IEEE Access.2022; 10: 131005.     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
  • Recent advances in the use of stimulated Raman scattering in histopathology
    Martin Lee, C. Simon Herrington, Manasa Ravindra, Kristel Sepp, Amy Davies, Alison N. Hulme, Valerie G. Brunton
    The Analyst.2021; 146(3): 789.     CrossRef
  • Preference and Demand for Digital Pathology and Computer-Aided Diagnosis among Korean Pathologists: A Survey Study Focused on Prostate Needle Biopsy
    Soo Jeong Nam, Yosep Chong, Chan Kwon Jung, Tae-Yeong Kwak, Ji Youl Lee, Jihwan Park, Mi Jung Rho, Heounjeong Go
    Applied Sciences.2021; 11(16): 7380.     CrossRef
  • An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features
    M. A. Aswathy, M. Jagannath
    Medical & Biological Engineering & Computing.2021; 59(9): 1773.     CrossRef
  • Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation
    Yosep Chong, Nishant Thakur, Ji Young Lee, Gyoyeon Hwang, Myungjin Choi, Yejin Kim, Hwanjo Yu, Mee Yon Cho
    Diagnostic Pathology.2021;[Epub]     CrossRef
  • Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images
    Hyun-Jong Jang, In-Hye Song, Sung-Hak Lee
    Cancers.2021; 13(15): 3811.     CrossRef
  • A novel evaluation method for Ki-67 immunostaining in paraffin-embedded tissues
    Eliane Pedra Dias, Nathália Silva Carlos Oliveira, Amanda Oliveira Serra-Campos, Anna Karoline Fausto da Silva, Licínio Esmeraldo da Silva, Karin Soares Cunha
    Virchows Archiv.2021; 479(1): 121.     CrossRef
  • Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade
    Andrew Lagree, Audrey Shiner, Marie Angeli Alera, Lauren Fleshner, Ethan Law, Brianna Law, Fang-I Lu, David Dodington, Sonal Gandhi, Elzbieta A. Slodkowska, Alex Shenfield, Katarzyna J. Jerzak, Ali Sadeghi-Naini, William T. Tran
    Current Oncology.2021; 28(6): 4298.     CrossRef
  • Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach
    Hyun-Jong Jang, Ahwon Lee, Jun Kang, In Hye Song, Sung Hak Lee
    World Journal of Gastroenterology.2021; 27(44): 7687.     CrossRef
  • Clustered nuclei splitting based on recurrent distance transform in digital pathology images
    Lukasz Roszkowiak, Anna Korzynska, Dorota Pijanowska, Ramon Bosch, Marylene Lejeune, Carlos Lopez
    EURASIP Journal on Image and Video Processing.2020;[Epub]     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
  • A bird’s-eye view of deep learning in bioimage analysis
    Erik Meijering
    Computational and Structural Biotechnology Journal.2020; 18: 2312.     CrossRef
  • Pathomics in urology
    Victor M. Schuettfort, Benjamin Pradere, Michael Rink, Eva Comperat, Shahrokh F. Shariat
    Current Opinion in Urology.2020; 30(6): 823.     CrossRef
  • Model Fooling Attacks Against Medical Imaging: A Short Survey
    Tuomo Sipola, Samir Puuska, Tero Kokkonen
    Information & Security: An International Journal.2020; 46(2): 215.     CrossRef
  • Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists
    Yosep Chong, Dae Cheol Kim, Chan Kwon Jung, Dong-chul Kim, Sang Yong Song, Hee Jae Joo, Sang-Yeop Yi
    Journal of Pathology and Translational Medicine.2020; 54(6): 437.     CrossRef
  • A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database
    Yosep Chong, Ji Young Lee, Yejin Kim, Jingyun Choi, Hwanjo Yu, Gyeongsin Park, Mee Yon Cho, Nishant Thakur
    Journal of Pathology and Translational Medicine.2020; 54(6): 462.     CrossRef
Original Articles
Colorectal epithelial neoplasm associated with gut-associated lymphoid tissue
Yo Han Jeon, Ji Hyun Ahn, Hee Kyung Chang
J Pathol Transl Med. 2020;54(2):135-145.   Published online January 29, 2020
DOI: https://doi.org/10.4132/jptm.2019.11.06
  • 5,990 View
  • 205 Download
AbstractAbstract PDF
Background
Colorectal epithelial neoplasm extending into the submucosal gut-associated lymphoid tissue (GALT) can cause difficulties in the differential diagnosis. Regarding GALT-associated epithelial neoplasms, a few studies favor the term “GALT carcinoma” while other studies have mentioned the term “GALT-associated pseudoinvasion/epithelial misplacement (PEM)”.
Methods
The clinicopathologic characteristics of 11 cases of colorectal epithelial neoplasm associated with submucosal GALT diagnosed via endoscopic submucosal dissection were studied.
Results
Eight cases (72.7%) were in males. The median age was 59 years, and age ranged from 53 to 73. All cases had a submucosal tumor component more compatible with GALT-associated PEM. Eight cases (72.7%) were located in the right colon. Ten cases (90.9%) had a non-protruding endoscopic appearance. Nine cases (81.8%) showed continuity between the submucosal and surface adenomatous components. Nine cases showed (81.8%) focal defects or discontinuation of the muscularis mucosae adjacent to the submucosal GALT. No case showed hemosiderin deposits in the submucosa or desmoplastic reaction. No case showed single tumor cells or small clusters of tumor cells in the submucosal GALT. Seven cases (63.6%) showed goblet cells in the submucosa. No cases showed oncocytic columnar cells lining submucosal glands.
Conclusions
Our experience suggests that pathologists should be aware of the differential diagnosis of GALT-associated submucosal extension by colorectal adenomatous neoplasm. Further studies are needed to validate classification of GALT-associated epithelial neoplasms.
Double cocktail immunostains with high molecular weight cytokeratin and GATA-3: useful stain to discriminate in situ involvement of prostatic ducts or acini from stromal invasion by urothelial carcinoma in the prostate
Junghye Lee, Youngeun Yoo, Sanghui Park, Min-Sun Cho, Sun Hee Sung, Jae Y. Ro
J Pathol Transl Med. 2020;54(2):146-153.   Published online February 10, 2020
DOI: https://doi.org/10.4132/jptm.2019.11.12
  • 5,205 View
  • 110 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDF
Background
Distinguishing prostatic stromal invasion (PSI) by urothelial carcinoma (UC) from in situ UC involving prostatic ducts or acini with no stromal invasion (in situ involvement) may be challenging on hematoxylin and eosin stained sections. However, the distinction between them is important because cases with PSI show worse prognosis. This study was performed to assess the utility of double cocktail immunostains with high molecular weight cytokeratin (HMWCK) and GATA-3 to discriminate PSI by UC from in situ UC involvement of prostatic ducts or acini in the prostate.
Methods
Among 117 radical cystoprostatectomy specimens for bladder UCs, 25 cases showed secondary involvement of bladder UC in prostatic ducts/acini only or associated stromal invasion and of these 25 cases, seven cases revealed equivocal PSI. In these seven cases with equivocal PSI, HMWCK, and GATA-3 double immunohistochemical stains were performed to identify whether this cocktail stain is useful to identify the stromal invasion.
Results
In all cases, basal cells of prostate glands showed strong cytoplasmic staining for HMWCK and UC cells showed strong nuclear staining for GATA-3. In cases with stromal invasion of UC, GATA-3-positive tumor cells in the prostatic stroma without surrounding HMWCK-positive basal cells were highlighted and easily recognized. Among seven equivocal cases, two cases showed PSI and five in situ UC in the prostate. In two cases, the original diagnoses were revised.
Conclusions
Our study suggested that HMWCK and GATA-3 double stains could be utilized as an adjunct method in the distinction between PSI by UC from in situ UC involving prostatic ducts or acini.

Citations

Citations to this article as recorded by  
  • Aberrant expression of GATA3 in metastatic adenocarcinoma of the prostate: an important pitfall
    João Lobo, Nazario P Tenace, Sofia Cañete‐Portillo, Isa Carneiro, Rui Henrique, Roberta Lucianò, Lara R Harik, Cristina Magi‐Galluzzi
    Histopathology.2024; 84(3): 507.     CrossRef
  • Utility of D2-40, Cytokeratin 5/6, and High–Molecular-weight Cytokeratin (Clone 34βE12) in Distinguishing Intraductal Spread of Urothelial Carcinoma From Prostatic Stromal Invasion
    Oleksii A. Iakymenko, Laurence M. Briski, Katiana S. Delma, Merce Jorda, Oleksandr N. Kryvenko
    American Journal of Surgical Pathology.2022; 46(4): 454.     CrossRef
Programmed death-ligand 1 expression and its correlation with clinicopathological parameters in gallbladder cancer
Ji Hye Kim, Kyungbin Kim, Misung Kim, Young Min Kim, Jae Hee Suh, Hee Jeong Cha, Hye Jeong Choi
J Pathol Transl Med. 2020;54(2):154-164.   Published online February 10, 2020
DOI: https://doi.org/10.4132/jptm.2019.11.13
  • 6,679 View
  • 153 Download
  • 14 Web of Science
  • 13 Crossref
AbstractAbstract PDF
Background
Immunomodulatory therapies targeting the interaction between programmed cell death protein 1 and programmed death-ligand 1 (PD-L1) have become increasingly important in anticancer treatment. Previous research on the subject of this immune response has established an association with tumor aggressiveness and a poor prognosis in certain cancers. Currently, scant information is available on the relationship between PD-L1 expression and gallbladder cancer (GBC).
Methods
We investigated the expression of PD-L1 in 101 primary GBC cases to determine the potential association with prognostic impact. PD-L1 expression was immunohistochemically assessed using a single PD-L1 antibody (clone SP263). Correlations with clinicopathological parameters, overall survival (OS), or progression- free survival (PFS) were analyzed.
Results
PD-L1 expression in tumor cells at cutoff levels of 1%, 10%, and 50% was present in 18.8%, 13.8%, and 7.9% of cases. Our study showed that positive PD-L1 expression at any cutoff was significantly correlated with poorly differentiated histologic grade and the presence of lymphovascular invasion (p < .05). PD-L1 expression at cutoff levels of 10% and 50% was significantly positive in patients with perineural invasion, higher T categories, and higher pathologic stages (p < .05). Additionally, there was a significant association noted between PD-L1 expression at a cutoff level of 50% and worse OS or PFS (p = .049 for OS, p = .028 for PFS). Other poor prognostic factors included histologic grade, T category, N category, pathologic stage, lymphovascular invasion, perineural invasion, growth pattern, and margin of resection (p < .05).
Conclusions
The expression of PD-L1 in GBC varies according to cutoff level but is valuably associated with poor prognostic parameters and survival. Our study indicates that the overexpression of PD-L1 in GBC had a negative prognostic impact.

Citations

Citations to this article as recorded by  
  • Lacking Immunotherapy Biomarkers for Biliary Tract Cancer: A Comprehensive Systematic Literature Review and Meta-Analysis
    Giorgio Frega, Fernando P. Cossio, Jesus M. Banales, Vincenzo Cardinale, Rocio I. R. Macias, Chiara Braconi, Angela Lamarca
    Cells.2023; 12(16): 2098.     CrossRef
  • Gallbladder carcinomas: review and updates on morphology, immunohistochemistry, and staging
    Whayoung Lee, Vishal S. Chandan
    Human Pathology.2023; 132: 149.     CrossRef
  • Prognostic Relevance of PDL1 and CA19-9 Expression in Gallbladder Cancer vs. Inflammatory Lesions
    Neetu Rawal, Supriya Awasthi, Nihar Ranjan Dash, Sunil Kumar, Prasenjit Das, Amar Ranjan, Anita Chopra, Maroof Ahmad Khan, Sundeep Saluja, Showket Hussain, Pranay Tanwar
    Current Oncology.2023; 30(2): 1571.     CrossRef
  • Identification of genes associated with gall bladder cell carcinogenesis: Implications in targeted therapy of gall bladder cancer
    Ishita Ghosh, Ruma Dey Ghosh, Soma Mukhopadhyay
    World Journal of Gastrointestinal Oncology.2023; 15(12): 2053.     CrossRef
  • CD73 and PD-L1 as Potential Therapeutic Targets in Gallbladder Cancer
    Lu Cao, Kim R. Bridle, Ritu Shrestha, Prashanth Prithviraj, Darrell H. G. Crawford, Aparna Jayachandran
    International Journal of Molecular Sciences.2022; 23(3): 1565.     CrossRef
  • Evolving Role of Immunotherapy in Advanced Biliary Tract Cancers
    Sandra Kang, Bassel F. El-Rayes, Mehmet Akce
    Cancers.2022; 14(7): 1748.     CrossRef
  • Novel immune scoring dynamic nomograms based on B7-H3, B7-H4, and HHLA2: Potential prediction in survival and immunotherapeutic efficacy for gallbladder cancer
    Chao Lv, Shukun Han, Baokang Wu, Zhiyun Liang, Yang Li, Yizhou Zhang, Qi Lang, Chongli Zhong, Lei Fu, Yang Yu, Feng Xu, Yu Tian
    Frontiers in Immunology.2022;[Epub]     CrossRef
  • PD-1 inhibitors plus nab-paclitaxel-containing chemotherapy for advanced gallbladder cancer in a second-line setting: A retrospective analysis of a case series
    Sirui Tan, Jing Yu, Qiyue Huang, Nan Zhou, Hongfeng Gou
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Expression of HER2 and Mismatch Repair Proteins in Surgically Resected Gallbladder Adenocarcinoma
    You-Na Sung, Sung Joo Kim, Sun-Young Jun, Changhoon Yoo, Kyu-Pyo Kim, Jae Hoon Lee, Dae Wook Hwang, Shin Hwang, Sang Soo Lee, Seung-Mo Hong
    Frontiers in Oncology.2021;[Epub]     CrossRef
  • Programmed Death Ligand-1 (PD-L1) Is an Independent Negative Prognosticator in Western-World Gallbladder Cancer
    Thomas Albrecht, Fritz Brinkmann, Michael Albrecht, Anke S. Lonsdorf, Arianeb Mehrabi, Katrin Hoffmann, Yakup Kulu, Alphonse Charbel, Monika N. Vogel, Christian Rupp, Bruno Köhler, Christoph Springfeld, Peter Schirmacher, Stephanie Roessler, Benjamin Goep
    Cancers.2021; 13(7): 1682.     CrossRef
  • Immune Microenvironment in Gallbladder Adenocarcinomas
    Pallavi A. Patil, Kara Lombardo, Weibiao Cao
    Applied Immunohistochemistry & Molecular Morphology.2021; 29(8): 557.     CrossRef
  • Molecular Targets and Emerging Therapies for Advanced Gallbladder Cancer
    Matteo Canale, Manlio Monti, Ilario Giovanni Rapposelli, Paola Ulivi, Francesco Giulio Sullo, Giulia Bartolini, Elisa Tiberi, Giovanni Luca Frassineti
    Cancers.2021; 13(22): 5671.     CrossRef
  • Overview of current targeted therapy in gallbladder cancer
    Xiaoling Song, Yunping Hu, Yongsheng Li, Rong Shao, Fatao Liu, Yingbin Liu
    Signal Transduction and Targeted Therapy.2020;[Epub]     CrossRef
Adjunctive markers for classification and diagnosis of central nervous system tumors: results of a multi-center neuropathological survey in Korea
Yoon Jin Cha, Se Hoon Kim, Na Rae Kim
J Pathol Transl Med. 2020;54(2):165-170.   Published online February 20, 2020
DOI: https://doi.org/10.4132/jptm.2020.02.04
  • 5,717 View
  • 206 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Background
The revised 4th 2016 World Health Organization (WHO) classification of tumors of the central nervous system (CNS) classification has adopted integrated diagnosis encompassing the histology and molecular features of CNS tumors. We aimed to investigate the immunohistochemistry, molecular testing, and testing methods for diagnosis of CNS tumors in pathological labs of tertiary centers in Korea, and evaluate the adequacy of tests for proper diagnosis in daily practice.
Methods
A survey, composed of eight questions concerning molecular testing for diagnosis of CNS tumors, was sent to 10 neuropathologists working in tertiary centers in Korea.
Results
For diagnosis of astrocytic and oligodendroglial tumors, all 10 centers performed isocitrate dehydrogenase mutations testing and 1p/19q loss of heterozygosity. For glioneuronal tumors, immunohistochemistry (IHC) assays for synaptophysin (n = 9), CD34 (n = 7), BRAF(VE1) (n = 5) were used. For embryonal tumors, particularly in medulloblastoma, four respondents used IHC panel (growth factor receptor bound protein 2-associated protein 1, filamin A, and yes-associated protein 1) for molecular subclassification. Regarding meningioma, all respondents performed Ki-67 IHC and five performed telomerase reverse transcriptase promoter mutation.
Conclusions
Most tertiary centers made proper diagnosis in line with 2016 WHO classification. As classification of CNS tumors has evolved to be more complex and more ancillary tests are required, these should be performed considering the effect of necessity and justification.

Citations

Citations to this article as recorded by  
  • Exploring the role of epidermal growth factor receptor variant III in meningeal tumors
    Rashmi Rana, Vaishnavi Rathi, Kirti Chauhan, Kriti Jain, Satnam Singh Chhabra, Rajesh Acharya, Samir Kumar Kalra, Anshul Gupta, Sunila Jain, Nirmal Kumar Ganguly, Dharmendra Kumar Yadav, Timir Tripathi
    PLOS ONE.2021; 16(9): e0255133.     CrossRef
Contribution of cytologic examination to diagnosis of poorly differentiated thyroid carcinoma
Na Rae Kim, Jae Yeon Seok, Yoo Seung Chung, Joon Hyop Lee, Dong Hae Chung
J Pathol Transl Med. 2020;54(2):171-178.   Published online February 5, 2020
DOI: https://doi.org/10.4132/jptm.2019.12.03
  • 5,759 View
  • 188 Download
  • 1 Web of Science
  • 4 Crossref
AbstractAbstract PDF
Background
The cytologic diagnosis of poorly differentiated thyroid carcinoma (PDTC) is difficult because it lacks salient cytologic findings and shares cytologic features with more commonly encountered neoplasms. Due to diverse cytologic findings and paucicellularity of PDTC, standardization of cytologic diagnostic criteria is limited. The purpose of this study is to investigate and recognize diverse thyroid findings of fine needle aspiration (FNA) cytology and frozen smear cytology in diagnosis of this rare but aggressive carcinoma.
Methods
The present study included six cases of FNA cytology and frozen smears of histologically diagnosed PDTCs.
Results
PDTC showed cytologic overlap with well-differentiated thyroid carcinomas (WDTCs). Five of six cases showed dedifferentiation arising from well differentiated thyroid carcinomas. Only one de novo PDTC showed highly cellular smears composed of discohesive small cells, high nuclear/cytoplasmic (N/C) ratio, prominent micronucleoli, and irregular nuclei. Retrospectively reviewed, these findings are highly suspicious for PDTC. Cytologic findings of nuclear atypia, pleomorphism, and irregularity were frequently found, whereas scattered small cells were seen only in the de novo case.
Conclusions
Heterogeneous cytologic findings of PDTCs are shared with those of WDTCs and contribute to difficult preoperative cytologic diagnoses. Most PDTCs show dedifferentiation from WDTCs. Albeit rare, de novo PDTC should be considered with cytology showing discohesive small cells with high N/C ratio. This will enable precise diagnosis and prompt treatment of this aggressive malignancy

Citations

Citations to this article as recorded by  
  • Non-papillary thyroid carcinoma diagnoses in The Bethesda System for Reporting Thyroid Cytopathology categories V and VI: An institutional experience
    Myunghee Kang, Na Rae Kim, Jae Yeon Seok
    Annals of Diagnostic Pathology.2024; 71: 152263.     CrossRef
  • An Unexpected Finding of Poorly Differentiated Thyroid Carcinoma in a Toxic Thyroid Nodule
    Kimberly Yuang, Huda Al-Bahadili, Alan Chang
    JCEM Case Reports.2023;[Epub]     CrossRef
  • Revisiting the cytomorphological features of poorly differentiated thyroid carcinoma: a comparative analysis with indeterminate thyroid fine-needle aspiration samples
    Yazeed Alwelaie, Ali Howaidi, Mohammed Tashkandi, Ahmad Almotairi, Hisham Saied, Moammar Muzzaffar, Doaa Alghamdi
    Journal of the American Society of Cytopathology.2023; 12(5): 331.     CrossRef
  • Characterization of the genomic alterations in poorly differentiated thyroid cancer
    Yeeun Lee, SeongRyeol Moon, Jae Yeon Seok, Joon-Hyop Lee, Seungyoon Nam, Yoo Seung Chung
    Scientific Reports.2023;[Epub]     CrossRef
Case Studies
Inconspicuous longitudinal tears of the intracranial vertebral artery in traumatic basal subarachnoid hemorrhage
Seongho Kim
J Pathol Transl Med. 2020;54(2):179-183.   Published online November 8, 2019
DOI: https://doi.org/10.4132/jptm.2019.10.15
  • 7,195 View
  • 175 Download
  • 1 Crossref
AbstractAbstract PDF
Blunt force trauma to the head or neck region can cause traumatic basal subarachnoid hemorrhage (TBSAH), which can result in rapid loss of consciousness and death; however, detecting such a vascular injury is difficult. Posterior neck dissection was performed to investigate the bleeding focus in TBSAH cases 2018 and 2019. In all four cases, autopsies revealed a longitudinal tear in the midsection of the vertebral artery’s intracranial portion. The midportion of the intracranial vertebral artery appears to be most vulnerable to TBSAH. Interestingly, three of the cases showed only a vaguely visible longitudinal fissure in the artery without a grossly apparent tear; rupture was confirmed by microscopic examination. Longitudinal fissures of the intracranial vertebral artery, which are difficult to identify without detailed examination, may be overlooked in some cases of TBSAH. Thus, careful gross and microscopic examination of the vertebral artery is recommended in cases of TBSAH.

Citations

Citations to this article as recorded by  
  • Effect of Ginseng Extract Ginsenoside Rg1 on Mice with Intracerebral Injury
    Zixin Zhuang, Jinman Chen, Hao Xu, Yongjun Wang, Qianqian Liang
    Chinese Medicine and Culture.2023;[Epub]     CrossRef
Primary carcinoid tumor in the external auditory canal
Dong Hae Chung, Gyu Cheol Han, Na Rae Kim
J Pathol Transl Med. 2020;54(2):184-187.   Published online November 13, 2019
DOI: https://doi.org/10.4132/jptm.2019.11.07
  • 4,987 View
  • 157 Download
  • 3 Web of Science
  • 2 Crossref
AbstractAbstract PDF
A 39-year-old man visited the department of otolaryngology due to an ongoing hearing disturbance that had lasted for 1 year. Temporal bone computed tomography revealed soft tissue density nearly obliterating the left external auditory canal (EAC). The mass was composed of sheets of round tumor cells containing moderate amounts of fine granular cytoplasm and salt and pepper chromatin. Neither mitosis nor necrosis was found. The Ki-67 proliferation index was less than 2%. Cells were positive for CD56 and synaptophysin but negative for chromogranin, cytokeratin (CK) 20, and CK7. Based on these findings, the tumor was diagnosed as a carcinoid tumor, well differentiated neuroendocrine carcinoma, grade 1 (G1) according to current World Health Organization (WHO) classification of head and neck tumors; and a neuroendocrine tumor, G1 according to neuroendocrine neoplasm (NEN)-2018 WHO standard classification. He remained free of local recurrence and metastasis after 20 months of follow up. To date, only six cases of primary NENs in the EAC have been reported. Metastatic tumor should be included in the differential diagnoses. Because of its rarity, the prognosis and treatment have not yet been clarified.

Citations

Citations to this article as recorded by  
  • Incidental finding of a neuroendocrine neoplasm in a suspected ear canal exostosis
    Alexander Wieck Fjaeldstad, Gerda Elisabeth Villadsen, Gitte Dam, Stephen Jacques Hamilton-Dutoit, Thomas Winther Frederiksen
    Otolaryngology Case Reports.2022; 22: 100394.     CrossRef
  • 68Ga-DOTATATE Uptake in Well-Differentiated Neuroendocrine Tumor of the External Auditory Canal
    Özge Erol Fenercioğlu, Ediz Beyhan, Rahime Şahin, Mehmet Can Baloğlu, Tevfik Fikret Çermik
    Clinical Nuclear Medicine.2022; 47(8): e552.     CrossRef
Brief Case Reports
Tumor-to-tumor metastasis: metastatic invasive lobular carcinoma of the breast within adenocarcinoma of the lung
Myoung Jae Kang, Ae Ri An, Myoung Ja Chung, Kyoung Min Kim
J Pathol Transl Med. 2020;54(2):188-191.   Published online September 16, 2019
DOI: https://doi.org/10.4132/jptm.2019.09.07
  • 4,157 View
  • 149 Download
  • 3 Web of Science
  • 4 Crossref
PDF

Citations

Citations to this article as recorded by  
  • Tumor-to-Tumor Metastases Involving Clear Cell Renal Cell Carcinomas: A Diagnostic Challenge for Pathologists Needing Clinical Correlation
    Claudia Manini, Claudia Provenza, Leire Andrés, Igone Imaz, Rosa Guarch, Raffaelle Nunziata, José I. López
    Clinics and Practice.2023; 13(1): 288.     CrossRef
  • Metástasis tumor a tumor en pulmón: reporte de tres casos y revisión de la literatura
    Paula Cristina Castro Quiroga, Blanca Viviana Fajardo Idrobo, Diana Marcela Caicedo Ruiz, Julieth Alexandra Franco Mira, Carlos Andrés Carvajal Fierro, Alfredo Ernesto Romero Rojas, Rafael Santiago Parra Medina
    Revista Colombiana de Cancerología.2023; 27(1): 107.     CrossRef
  • Lobular to Lobule: Metastatic Breast Carcinoma to Olfactory Neuroblastoma
    Kent M. Swimley, Silvana Di Palma, Lester D. R. Thompson
    Head and Neck Pathology.2021; 15(2): 642.     CrossRef
  • A case of colorectal cancer with intratumoral metastasis to primary lung cancer
    Yasushi Cho, Mitsuhito Kaji, Shunsuke Nomura, Yusuke Motohashi, Masaaki Sato, Motoya Takeuchi
    The Journal of the Japanese Association for Chest Surgery.2021; 35(5): 576.     CrossRef
Pseudomesotheliomatous carcinoma of the lung in the parietal pleura
Ae Ri An, Kyoung Min Kim, Jong Hun Kim, Gong Yong Jin, Young Hoon Choe, Myoung Ja Chung
J Pathol Transl Med. 2020;54(2):192-195.   Published online January 29, 2020
DOI: https://doi.org/10.4132/jptm.2019.11.14
  • 4,612 View
  • 130 Download
  • 1 Crossref
PDF

Citations

Citations to this article as recorded by  
  • Pseudomesotheliomatous Carcinoma of the Lung with Morphological Characteristics of Signet Ring Cell Carcinoma: An Autopsy Case Report
    Tetsu Hirakawa, Takuya Tanimoto, Yui Hattori, Ryo Katsura, Shinya Miyake, Suguru Fujita, Sayaka Ueno, Ken Masuda, Takashi Nishisaka, Nobuhisa Ishikawa
    Internal Medicine.2023;[Epub]     CrossRef
Corrigendum
Correction of acknowledgments: PD-L1 testing in non-small cell lung cancer: past, present, and future
Hyojin Kim, Jin-Haeng Chung
J Pathol Transl Med. 2020;54(2):196-196.   Published online March 10, 2020
DOI: https://doi.org/10.4132/jptm.2020.02.17
Corrects: J Pathol Transl Med 2019;53(4):199
  • 3,492 View
  • 74 Download
  • 1 Crossref
PDF

Citations

Citations to this article as recorded by  
  • ORC6 acts as an effective prognostic predictor for non‑small cell lung cancer and is closely associated with tumor progression
    Letian Chen, Dongdong Zhang, Yujuan Chen, Huilan Zhu, Zhipeng Liu, Zhiping Yu, Junping Xie
    Oncology Letters.2024;[Epub]     CrossRef

J Pathol Transl Med : Journal of Pathology and Translational Medicine