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Case Study
EWSR1 rearranged primary renal myoepithelial carcinoma: a diagnostic conundrum
Nilay Nishith, Zachariah Chowdhury
J Pathol Transl Med. 2023;57(5):284-288.   Published online September 15, 2023
DOI: https://doi.org/10.4132/jptm.2023.08.08
  • 896 View
  • 171 Download
AbstractAbstract PDF
Primary renal myoepithelial carcinoma is an exceedingly rare neoplasm with an aggressive phenotype and Ewing sarcoma breakpoint region 1 (EWSR1) rearrangement in a small fraction of cases. In addition to its rarity, the diagnosis can be challenging for the pathologist due to morphologic heterogeneity, particularly on the biopsy specimen. At times, immunohistochemistry may be indecisive; therefore, molecular studies should be undertaken for clinching the diagnosis. We aim to illustrate a case of primary myoepithelial carcinoma of the kidney with EWSR1-rearrangement in a 67-year-old male patient who presented with right supraclavicular mass, which was clinically diagnosed as carcinoma of an unknown primary. An elaborate immunohistochemical work-up aided by fluorescent in-situ hybridization allowed us to reach a conclusive diagnosis. This unusual case report advocates that one should be aware of the histological mimickers and begin with broad differential diagnoses alongside sporadic ones and then narrow them down with appropriate ancillary studies.
Original Article
Loss of aquaporin-1 expression is associated with worse clinical outcomes in clear cell renal cell carcinoma: an immunohistochemical study
Seokhyeon Lee, Bohyun Kim, Minsun Jung, Kyung Chul Moon
J Pathol Transl Med. 2023;57(4):232-237.   Published online July 11, 2023
DOI: https://doi.org/10.4132/jptm.2023.06.17
  • 1,290 View
  • 116 Download
AbstractAbstract PDF
Background
Aquaporin (AQP) expression has been investigated in various malignant neoplasms, and the overexpression of AQP is related to poor prognosis in some malignancies. However, the expression of AQP protein in clear cell renal cell carcinoma (ccRCC) has not been extensively investigated by immunohistochemistry with large sample size.
Methods
We evaluated the AQP expression in 827 ccRCC with immunohistochemical staining in tissue microarray blocks and classified the cases into two categories, high and low expression.
Results
High expression of aquaporin-1 (AQP1) was found in 320 cases (38.7%), but aquaporin-3 was not expressed in ccRCC. High AQP1 expression was significantly related to younger age, low TNM stage, low World Health Organization/International Society of Urologic Pathology nuclear grade, and absence of distant metastasis. Furthermore, high AQP1 expression was also significantly associated with longer overall survival (OS; p<.001) and progression-specific survival (PFS; p<.001) and was an independent predictor of OS and PFS in ccRCC.
Conclusions
Our study revealed the prognostic significance of AQP1 protein expression in ccRCC. These findings could be applied to predict the prognosis of ccRCC.
Review
Noninvasive follicular thyroid neoplasm with papillary-like nuclear features: its updated diagnostic criteria, preoperative cytologic diagnoses and impact on the risk of malignancy
Hee Young Na, So Yeon Park
J Pathol Transl Med. 2022;56(6):319-325.   Published online November 9, 2022
DOI: https://doi.org/10.4132/jptm.2022.09.29
  • 2,157 View
  • 171 Download
  • 4 Web of Science
  • 4 Crossref
AbstractAbstract PDF
Due to the extremely indolent behavior, a subset of noninvasive encapsulated follicular variant papillary thyroid carcinomas has been classified as “noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP)” since 2016 and is no longer considered carcinoma. Since the introduction of this new terminology, changes and refinements have been made in diagnostic criteria. Initially, the incidence of NIFTP was estimated substantial. However, the reported incidence of NIFTP varies greatly among studies and regions, with higher incidence in North American and European countries than in Asian countries. Thus, the changes in the risk of malignancy (ROM) in the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) differ inevitably among regions. Because more conservative surgery is recommended for NIFTPs, distinguishing NIFTPs from papillary thyroid carcinomas in preoperative fine-needle aspiration cytology became one of the major concerns. This review will provide comprehensive overview of updates on diagnostic criteria, actual incidence and preoperative cytologic diagnoses of NIFTP, and its impact on the ROM in TBSRTC.

Citations

Citations to this article as recorded by  
  • Preoperative evaluation of thyroid nodules – Diagnosis and management strategies
    Tapoi Dana Antonia, Lambrescu Ioana Maria, Gheorghisan-Galateanu Ancuta-Augustina
    Pathology - Research and Practice.2023; 246: 154516.     CrossRef
  • Reevaluating diagnostic categories and associated malignancy risks in thyroid core needle biopsy
    Chan Kwon Jung
    Journal of Pathology and Translational Medicine.2023; 57(4): 208.     CrossRef
  • Strategies for Treatment of Thyroid Cancer
    Deepika Yadav, Pramod Kumar Sharma, Rishabha Malviya, Prem Shankar Mishra
    Current Drug Targets.2023; 24(5): 406.     CrossRef
  • Identification of NIFTP-Specific mRNA Markers for Reliable Molecular Diagnosis of Thyroid Tumors
    So-Yeon Lee, Jong-Lyul Park, Kwangsoon Kim, Ja Seong Bae, Jae-Yoon Kim, Seon-Young Kim, Chan Kwon Jung
    Endocrine Pathology.2023; 34(3): 311.     CrossRef
Original Articles
Diagnostic distribution and pitfalls of glandular abnormalities in cervical cytology: a 25-year single-center study
Jung-A Sung, Ilias P. Nikas, Haeryoung Kim, Han Suk Ryu, Cheol Lee
J Pathol Transl Med. 2022;56(6):354-360.   Published online November 9, 2022
DOI: https://doi.org/10.4132/jptm.2022.09.05
  • 1,745 View
  • 94 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDF
Background
Detection of glandular abnormalities in Papanicolaou (Pap) tests is challenging. This study aimed to review our institute’s experience interpreting such abnormalities, assess cytohistologic concordance, and identify cytomorphologic features associated with malignancy in follow-up histology.
Methods
Patients with cytologically-detected glandular lesions identified in our pathology records from 1995 to 2020 were included in this study.
Results
Of the 683,197 Pap tests performed, 985 (0.144%) exhibited glandular abnormalities, 657 of which had tissue follow-up available. One hundred eighty-eight cases were cytologically interpreted as adenocarcinoma and histologically diagnosed as malignant tumors of various origins. There were 213 cases reported as atypical glandular cells (AGC) and nine cases as adenocarcinoma in cytology, yet they were found to be benign in follow-up histology. In addition, 48 cases diagnosed with AGC and six with adenocarcinoma cytology were found to have cervical squamous lesions in follow-up histology, including four squamous cell carcinomas. Among the cytomorphological features examined, nuclear membrane irregularity, three-dimensional clusters, single-cell pattern, and presence of mitoses were associated with malignant histology in follow-up.
Conclusions
This study showed our institute’s experience detecting glandular abnormalities in cervical cytology over a 25-year period, revealing the difficulty of this task. Nonetheless, the present study indicates that several cytological findings such as membrane irregularity, three-dimensional clusters, single-cell pattern, and evidence of proliferation could help distinguishing malignancy from a benign lesion.

Citations

Citations to this article as recorded by  
  • Analysis of atypical glandular cells in ThinPrep Pap smear and follow-up histopathology
    Tengfei Wang, Yinan Hua, Lina Liu, Bing Leng
    Baylor University Medical Center Proceedings.2024; : 1.     CrossRef
Cytopathologic features of human papillomavirus–independent, gastric-type endocervical adenocarcinoma
Min-Kyung Yeo, Go Eun Bae, Dong-Hyun Kim, In-Ock Seong, Kwang-Sun Suh
J Pathol Transl Med. 2022;56(5):260-269.   Published online September 13, 2022
DOI: https://doi.org/10.4132/jptm.2022.07.05
  • 1,918 View
  • 118 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDF
Background
Gastric-type endocervical adenocarcinoma (GEA) is unrelated to human papillomavirus (HPV) infection and is clinically aggressive compared with HPV-associated usual-type endocervical adenocarcinoma (UEA). The cytological diagnosis falls short of a definitive diagnosis of GEA and is often categorized as atypical glandular cells (AGCs). To improve cytologic recognition, cytological findings of HPV-independent GEA were analyzed and the results compared with HPV-associated UEA.
Methods
Cervical Papanicolaou (Pap) smears from eight patients with a histopathologic diagnosis of GEA and 12 control cases of UEA were reviewed. All slides were conventionally prepared and/or liquid-based prepared (ThinPrep) and stained following the Pap method. A mucinous background, architectural, nuclear, and cytoplasmic features were analyzed and compared with UEA.
Results
Preoperative cytologic diagnoses of the eight GEA cases were AGCs, favor neoplastic in three cases, adenocarcinoma in situ in one case, and adenocarcinoma in four cases. Cytologically, monolayered honeycomb-like sheets (p = .002) of atypical endocervical cells with vacuolar granular cytoplasm (p = .001) were extensive in GEA, and three-dimensional clusters (p = .010) were extensive in UEA. Although the differences were not statistically significant, background mucin (p = .058), vesicular nuclei (p = .057), and golden-brown intracytoplasmic mucin (p = .089) were also discriminatory findings for GEA versus UEA.
Conclusions
Although GEA is difficult to diagnose on cytologic screening, GEA can be recognized based on cytologic features of monolayered honeycomb sheets of atypical endocervical cells with abundant vacuolar cytoplasm and some golden-brown intracytoplasmic mucin. UEA cases are characterized by three-dimensional clusters.

Citations

Citations to this article as recorded by  
  • Risk Factors Affecting Clinical Outcomes of Low-risk Early-stage Human Papillomavirus–Associated Endocervical Adenocarcinoma Treated by Surgery Alone: Application of Silva Pattern
    Bong Kyung Bae, Hyunsik Bae, Won Kyung Cho, Byoung-Gie Kim, Chel Hun Choi, Tae-Joong Kim, Yoo-Young Lee, Jeong-Won Lee, Hyun-Soo Kim, Won Park
    International Journal of Gynecological Pathology.2024;[Epub]     CrossRef
Deep learning for computer-assisted diagnosis of hereditary diffuse gastric cancer
Sean A. Rasmussen, Thomas Arnason, Weei-Yuarn Huang
J Pathol Transl Med. 2021;55(2):118-124.   Published online January 22, 2021
DOI: https://doi.org/10.4132/jptm.2020.12.22
  • 2,708 View
  • 116 Download
  • 5 Web of Science
  • 5 Crossref
AbstractAbstract PDF
Background
Patients with hereditary diffuse gastric cancer often undergo prophylactic gastrectomy to minimize cancer risk. Because intramucosal poorly cohesive carcinomas in this setting are typically not grossly visible, many pathologists assess the entire gastrectomy specimen microscopically. With 150 or more slides per case, this is a major time burden for pathologists. This study utilizes deep learning methods to analyze digitized slides and detect regions of carcinoma.
Methods
Prophylactic gastrectomy specimens from seven patients with germline CDH1 mutations were analyzed (five for training/validation and two for testing, with a total of 133 tumor foci). All hematoxylin and eosin slides containing cancer foci were digitally scanned, and patches of size 256×256 pixels were randomly extracted from regions of cancer as well as from regions of normal background tissue, resulting in 15,851 images for training/validation and 970 images for testing. A model with DenseNet-169 architecture was trained for 150 epochs, then evaluated on images from the test set. External validation was conducted on 814 images scanned at an outside institution.
Results
On individual patches, the trained model achieved a receiver operating characteristic (ROC) area under the curve (AUC) of 0.9986. This enabled it to maintain a sensitivity of 90% with a false-positive rate of less than 0.1%. On the external validation dataset, the model achieved a similar ROC AUC of 0.9984. On whole slide images, the network detected 100% of tumor foci and correctly eliminated an average of 99.9% of the non-cancer slide area from consideration.
Conclusions
Overall, our model shows encouraging progress towards computer-assisted diagnosis of hereditary diffuse gastric cancer.

Citations

Citations to this article as recorded by  
  • Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace
    Guoyang Zhang, Jingjing Song, Zongfeng Feng, Wentao Zhao, Pan Huang, Li Liu, Yang Zhang, Xufeng Su, Yukang Wu, Yi Cao, Zhengrong Li, Zhigang Jie
    Frontiers in Oncology.2023;[Epub]     CrossRef
  • Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer
    Anqi Wang, Jieli Zhou, Gang Wang, Beibei Zhang, Hongyi Xin, Haiyang Zhou
    Asian Journal of Surgery.2023; 46(9): 3568.     CrossRef
  • Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review
    Marianne Linley L. Sy-Janairo, Jose Isagani B. Janairo
    Journal of Gastrointestinal Cancer.2023;[Epub]     CrossRef
  • Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance
    Yuanqing Yang, Kai Sun, Yanhua Gao, Kuansong Wang, Gang Yu
    Diagnostics.2023; 13(19): 3115.     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
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
J Pathol Transl Med. 2020;54(6):462-470.   Published online August 31, 2020
DOI: https://doi.org/10.4132/jptm.2020.07.11
  • 3,984 View
  • 105 Download
  • 6 Web of Science
  • 6 Crossref
AbstractAbstract PDFSupplementary Material
Background
Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms.
Methods
A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets.
Results
IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases.
Conclusions
Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease- specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

Citations

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  • Real-Life Barriers to Diagnosis of Early Mycosis Fungoides: An International Expert Panel Discussion 
    Emmilia Hodak, Larisa Geskin, Emmanuella Guenova, Pablo L. Ortiz-Romero, Rein Willemze, Jie Zheng, Richard Cowan, Francine Foss, Cristina Mangas, Christiane Querfeld
    American Journal of Clinical Dermatology.2023; 24(1): 5.     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
  • Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives
    Dai Chihara, Loretta J. Nastoupil, Christopher R. Flowers
    British Journal of Haematology.2023; 202(2): 219.     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 Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review
    Nishant Thakur, Mohammad Rizwan Alam, Jamshid Abdul-Ghafar, Yosep Chong
    Cancers.2022; 14(14): 3529.     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
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
  • 14,275 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

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  • 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
Comparison of papanicolaou smear and human papillomavirus (HPV) test as cervical screening tools: can we rely on HPV test alone as a screening method? An 11-year retrospective experience at a single institution
Myunghee Kang, Seung Yeon Ha, Hyun Yee Cho, Dong Hae Chung, Na Rae Kim, Jungsuk An, Sangho Lee, Jae Yeon Seok, Juhyeon Jeong
J Pathol Transl Med. 2020;54(1):112-118.   Published online January 15, 2020
DOI: https://doi.org/10.4132/jptm.2019.11.29
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AbstractAbstract PDF
Background
The decrease in incidence of cervical dysplasia and carcinoma has not been as dramatic as expected with the development of improved research tools and test methods. The human papillomavirus (HPV) test alone has been suggested for screening in some countries. The National Cancer Screening Project in Korea has applied Papanicolaou smears (Pap smears) as the screening method for cervical dysplasia and carcinoma. We evaluated the value of Pap smear and HPV testing as diagnostic screening tools in a single institution.
Methods
Patients co-tested with HPV test and Pap smear simultaneously or within one month of each other were included in this study. Patients with only punch biopsy results were excluded because of sampling errors. A total of 999 cases were included, and the collected reports encompassed results of smear cytology, HPV subtypes, and histologic examinations.
Results
Sensitivity and specificity of detecting high-grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC) were higher for Pap smears than for HPV tests (sensitivity, 97.14%; specificity, 85.58% for Pap smears; sensitivity, 88.32%; specificity, 54.92% for HPV tests). HPV tests and Pap smears did not differ greatly in detection of low-grade squamous intraepithelial lesion (85.35% for HPV test, 80.31% for Pap smears). When atypical glandular cells were noted on Pap smears, the likelihood for histologic diagnosis of adenocarcinoma following Pap smear was higher than that of high-risk HPV test results (18.8 and 1.53, respectively).
Conclusions
Pap smears were more useful than HPV tests in the diagnosis of HSIL, SCC, and glandular lesions.

Citations

Citations to this article as recorded by  
  • Challenges in the diagmosis of cervical pathologies
    D. Y. Chernov, O. A. Tikhonovskaya, S. V. Logvinov, I. A. Petrov, Y. S. Yuriev, A. A. Zhdankina, A. V. Gerasimov, I. V. Zingalyuk, G. A. Mikheenko
    Bulletin of Siberian Medicine.2024; 22(4): 201.     CrossRef
  • Selection of endogenous control and identification of significant microRNA deregulations in cervical cancer
    T. Stverakova, I. Baranova, P. Mikyskova, B. Gajdosova, H. Vosmikova, J. Laco, V. Palicka, H. Parova
    Frontiers in Oncology.2023;[Epub]     CrossRef
  • Attitudes towards prevention of cervical cancer and early diagnosis among female academicians
    Nurhan Doğan, Gamze Fışkın
    Journal of Obstetrics and Gynaecology Research.2022; 48(6): 1433.     CrossRef
  • Role of Self-Sampling for Cervical Cancer Screening: Diagnostic Test Properties of Three Tests for the Diagnosis of HPV in Rural Communities of Cuenca, Ecuador
    Bernardo Vega Crespo, Vivian Alejandra Neira, José Ortíz Segarra, Ruth Maldonado Rengel, Diana López, María Paz Orellana, Andrea Gómez, María José Vicuña, Jorge Mejía, Ina Benoy, Tesifón Parrón Carreño, Veronique Verhoeven
    International Journal of Environmental Research and Public Health.2022; 19(8): 4619.     CrossRef
  • Utility of Scoring System for Screening and Early Warning of Cervical Cancer Based on Big Data Analysis
    Dan Hou, Binjie Yang, Yangdan Li, Ming Sun
    Frontiers in Public Health.2022;[Epub]     CrossRef
  • Evaluation of Urine and Vaginal Self-Sampling versus Clinician-Based Sampling for Cervical Cancer Screening: A Field Comparison of the Acceptability of Three Sampling Tests in a Rural Community of Cuenca, Ecuador
    Bernardo Vega Crespo, Vivian Alejandra Neira, José Ortíz S, Ruth Maldonado-Rengel, Diana López, Andrea Gómez, María José Vicuña, Jorge Mejía, Ina Benoy, Tesifón Parrón Carreño, Veronique Verhoeven
    Healthcare.2022; 10(9): 1614.     CrossRef
  • Diagnostic distribution and pitfalls of glandular abnormalities in cervical cytology: a 25-year single-center study
    Jung-A Sung, Ilias P. Nikas, Haeryoung Kim, Han Suk Ryu, Cheol Lee
    Journal of Pathology and Translational Medicine.2022; 56(6): 354.     CrossRef
  • Comparison of Learning Transfer Using Simulation Problem-Based Learning and Demonstration: An Application of Papanicolaou Smear Nursing Education
    Jeongim Lee, Hae Kyoung Son
    International Journal of Environmental Research and Public Health.2021; 18(4): 1765.     CrossRef
  • Investigating host-virus interaction mechanism and phylogenetic analysis of viral proteins involved in the pathogenesis
    Ahmad Abu Turab Naqvi, Farah Anjum, Alaa Shafie, Sufian Badar, Abdelbaset Mohamed Elasbali, Dharmendra Kumar Yadav, Md. Imtaiyaz Hassan, Timir Tripathi
    PLOS ONE.2021; 16(12): e0261497.     CrossRef
  • Utility of Human Papillomavirus Testing for Cervical Cancer Screening in Korea
    Mee-seon Kim, Eun Hee Lee, Moon-il Park, Jae Seok Lee, Kisu Kim, Mee Sook Roh, Hyoun Wook Lee
    International Journal of Environmental Research and Public Health.2020; 17(5): 1726.     CrossRef
A Multi-institutional Study of Prevalence and Clinicopathologic Features of Non-invasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features (NIFTP) in Korea
Ja Yeong Seo, Ji Hyun Park, Ju Yeon Pyo, Yoon Jin Cha, Chan Kwon Jung, Dong Eun Song, Jeong Ja Kwak, So Yeon Park, Hee Young Na, Jang-Hee Kim, Jae Yeon Seok, Hee Sung Kim, Soon Won Hong
J Pathol Transl Med. 2019;53(6):378-385.   Published online October 21, 2019
DOI: https://doi.org/10.4132/jptm.2019.09.18
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AbstractAbstract PDF
Background
In the present multi-institutional study, the prevalence and clinicopathologic characteristics of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) were evaluated among Korean patients who underwent thyroidectomy for papillary thyroid carcinoma (PTC).
Methods
Data from 18,819 patients with PTC from eight university hospitals between January 2012 and February 2018 were retrospectively evaluated. Pathology reports of all PTCs and slides of potential NIFTP cases were reviewed. The strict criterion of no papillae was applied for the diagnosis of NIFTP. Due to assumptions regarding misclassification of NIFTP as non-PTC tumors, the lower boundary of NIFTP prevalence among PTCs was estimated. Mutational analysis for BRAF and three RAS isoforms was performed in 27 randomly selected NIFTP cases.
Results
The prevalence of NIFTP was 1.3% (238/18,819) of all PTCs when the same histologic criteria were applied for NIFTP regardless of the tumor size but decreased to 0.8% (152/18,819) when tumors ≥1 cm in size were included. The mean follow-up was 37.7 months and no patient with NIFTP had evidence of lymph node metastasis, distant metastasis, or disease recurrence during the follow-up period. A difference in prevalence of NIFTP before and after NIFTP introduction was not observed. BRAFV600E mutation was not found in NIFTP. The mutation rate for the three RAS genes was 55.6% (15/27).
Conclusions
The low prevalence and indolent clinical outcome of NIFTP in Korea was confirmed using the largest number of cases to date. The introduction of NIFTP may have a small overall impact in Korean practice.

Citations

Citations to this article as recorded by  
  • Analysis of a pre-2017 follicular variant papillary thyroid carcinoma cohort reclassified as noninvasive follicular thyroid neoplasm with papillary-like features (NIFTP): an 11-year retrospective single institution experience
    Shaham Beg, Sana Irfan Khan, Isabella Cui, Theresa Scognamiglio, Rema Rao
    Journal of the American Society of Cytopathology.2023; 12(2): 112.     CrossRef
  • Noninvasive Follicular Thyroid Neoplasm With Papillary-Like Nuclear Features: What a Surgeon Should Know
    Jabir Alharbi, Thamer Alraddadi, Haneen Sebeih, Mohammad A Alessa, Haddad H Alkaf, Ahmed Bahaj, Sherif K Abdelmonim
    Cureus.2023;[Epub]     CrossRef
  • NTRK Fusion in a Cohort of BRAF p. V600E Wild-Type Papillary Thyroid Carcinomas
    Seung Eun Lee, Mi-Sook Lee, Heejin Bang, Mi Young Kim, Yoon-La Choi, Young Lyun Oh
    Modern Pathology.2023; 36(8): 100180.     CrossRef
  • A Comprehensive Study on the Diagnosis and Management of Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features
    Bayan A. Alzumaili, Lauren N. Krumeich, Reagan Collins, Timothy Kravchenko, Emad I. Ababneh, Adam S. Fisch, William C. Faquin, Vania Nosé, Maria Martinez-Lage, Gregory W. Randolph, Rajshri M. Gartland, Carrie C. Lubitz, Peter M. Sadow
    Thyroid.2023; 33(5): 566.     CrossRef
  • Clinical-Pathological and Molecular Evaluation of 451 NIFTP Patients from a Single Referral Center
    Paola Vignali, Agnese Proietti, Elisabetta Macerola, Anello Marcello Poma, Liborio Torregrossa, Clara Ugolini, Alessio Basolo, Antonio Matrone, Teresa Rago, Ferruccio Santini, Rossella Elisei, Gabriele Materazzi, Fulvio Basolo
    Cancers.2022; 14(2): 420.     CrossRef
  • Noninvasive follicular thyroid neoplasm with papillary-like nuclear features: its updated diagnostic criteria, preoperative cytologic diagnoses and impact on the risk of malignancy
    Hee Young Na, So Yeon Park
    Journal of Pathology and Translational Medicine.2022; 56(6): 319.     CrossRef
  • SFE-AFCE-SFMN 2022 Consensus on the management of thyroid nodules : Follow-up: How and how long?
    Sophie Leboulleux, Livia Lamartina, Emmanuelle Lecornet Sokol, Fabrice Menegaux, Laurence Leenhardt, Gilles Russ
    Annales d'Endocrinologie.2022; 83(6): 407.     CrossRef
  • Different Threshold of Malignancy for RAS-like Thyroid Tumors Causes Significant Differences in Thyroid Nodule Practice
    Kennichi Kakudo
    Cancers.2022; 14(3): 812.     CrossRef
  • Clinicopathological parameters for predicting non-invasive follicular thyroid neoplasm with papillary features (NIFTP)
    Eunju Jang, Kwangsoon Kim, Chan Kwon Jung, Ja Seong Bae, Jeong Soo Kim
    Therapeutic Advances in Endocrinology and Metabolism.2021; 12: 204201882110005.     CrossRef
  • The Incidence of Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features: A Meta-Analysis Assessing Worldwide Impact of the Reclassification
    Chanchal Rana, Huy Gia Vuong, Thu Quynh Nguyen, Hoang Cong Nguyen, Chan Kwon Jung, Kennichi Kakudo, Andrey Bychkov
    Thyroid.2021;[Epub]     CrossRef
  • The Genomic Landscape of Thyroid Cancer Tumourigenesis and Implications for Immunotherapy
    Amandeep Singh, Jeehoon Ham, Joseph William Po, Navin Niles, Tara Roberts, Cheok Soon Lee
    Cells.2021; 10(5): 1082.     CrossRef
  • Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is rare, benign lesion using modified stringent diagnostic criteria: Reclassification and outcome study
    David Cubero Rego, Hwajeong Lee, Anne Boguniewicz, Timothy A. Jennings
    Annals of Diagnostic Pathology.2020; 44: 151439.     CrossRef
  • Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features: From Echography to Genetic Profile
    Francesca Maletta, Enrico Costantino Falco, Alessandro Gambella, Jasna Metovic, Mauro Papotti
    The Tohoku Journal of Experimental Medicine.2020; 252(3): 209.     CrossRef
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
  • 22,872 View
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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.

Citations

Citations to this article as recorded by  
  • 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
  • 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
  • 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.2023;[Epub]     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
  • Automated abstraction of myocardial perfusion imaging reports using natural language processing
    Parija Sharedalal, Ajay Singh, Neal Shah, Diwakar Jain
    Journal of Nuclear Cardiology.2022; 29(3): 1188.     CrossRef
  • Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer
    Geetanjali Saini, Shriya Joshi, Chakravarthy Garlapati, Hongxiao Li, Jun Kong, Jayashree Krishnamurthy, Michelle D. Reid, Ritu Aneja
    Seminars in Cancer Biology.2022; 81: 220.     CrossRef
  • A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
    Yixin Li, Chen Li, Xiaoyan Li, Kai Wang, Md Mamunur Rahaman, Changhao Sun, Hao Chen, Xinran Wu, Hong Zhang, Qian Wang
    Archives of Computational Methods in Engineering.2022; 29(1): 609.     CrossRef
  • Artificial intelligence in oncology: From bench to clinic
    Jamal Elkhader, Olivier Elemento
    Seminars in Cancer Biology.2022; 84: 113.     CrossRef
  • Yeast‐like organisms phagocytosed by circulating neutrophils: Evidence of disseminated histoplasmosis
    Yue Zhao, Jenna McCracken, Endi Wang
    International Journal of Laboratory Hematology.2022; 44(1): 51.     CrossRef
  • Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review
    Aleksandra Zuraw, Famke Aeffner
    Veterinary Pathology.2022; 59(1): 6.     CrossRef
  • A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
    Xintong Li, Chen Li, Md Mamunur Rahaman, Hongzan Sun, Xiaoqi Li, Jian Wu, Yudong Yao, Marcin Grzegorzek
    Artificial Intelligence Review.2022; 55(6): 4809.     CrossRef
  • Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification
    Octav Ginghina, Ariana Hudita, Marius Zamfir, Andrada Spanu, Mara Mardare, Irina Bondoc, Laura Buburuzan, Sergiu Emil Georgescu, Marieta Costache, Carolina Negrei, Cornelia Nitipir, Bianca Galateanu
    Frontiers in Oncology.2022;[Epub]     CrossRef
  • Automated bone marrow cytology using deep learning to generate a histogram of cell types
    Rohollah Moosavi Tayebi, Youqing Mu, Taher Dehkharghanian, Catherine Ross, Monalisa Sur, Ronan Foley, Hamid R. Tizhoosh, Clinton J. V. Campbell
    Communications Medicine.2022;[Epub]     CrossRef
  • Risultati di esami di laboratorio per intelligenza artificiale e "machine learning"
    Marco PRADELLA
    La Rivista Italiana della Medicina di Laboratorio.2022;[Epub]     CrossRef
  • The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach
    Florian Funer
    Medicine, Health Care and Philosophy.2022; 25(2): 167.     CrossRef
  • Deep discriminative learning model with calibrated attention map for the automated diagnosis of diffuse large B-cell lymphoma
    Sautami Basu, Ravinder Agarwal, Vishal Srivastava
    Biomedical Signal Processing and Control.2022; 76: 103728.     CrossRef
  • Question and Answer Techniques for Financial Audits in Universities Based on Deep Learning
    Qiang Li, Hangjun Che
    Mathematical Problems in Engineering.2022; 2022: 1.     CrossRef
  • Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study
    Erdenebayar Urtnasan, Jung Hun Lee, Byungjin Moon, Hee Young Lee, Kyuhee Lee, Hyun Youk
    JMIR Medical Informatics.2022; 10(6): e34724.     CrossRef
  • Impact of artificial intelligence on pathologists’ decisions: an experiment
    Julien Meyer, April Khademi, Bernard Têtu, Wencui Han, Pria Nippak, David Remisch
    Journal of the American Medical Informatics Association.2022; 29(10): 1688.     CrossRef
  • Rapid Screening Using Pathomorphologic Interpretation to Detect BRAFV600E Mutation and Microsatellite Instability in Colorectal Cancer
    Satoshi Fujii, Daisuke Kotani, Masahiro Hattori, Masato Nishihara, Toshihide Shikanai, Junji Hashimoto, Yuki Hama, Takuya Nishino, Mizuto Suzuki, Ayatoshi Yoshidumi, Makoto Ueno, Yoshito Komatsu, Toshiki Masuishi, Hiroki Hara, Taito Esaki, Yoshiaki Nakamu
    Clinical Cancer Research.2022; 28(12): 2623.     CrossRef
  • Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry
    Sean A. Rasmussen, Valerie J. Taylor, Alexi P. Surette, Penny J. Barnes, Gillian C. Bethune
    Applied Immunohistochemistry & Molecular Morphology.2022; 30(10): 668.     CrossRef
  • Deep Neural Network for the Prediction of KRAS Genotype in Rectal Cancer
    Waleed M Ghareeb, Eman Draz, Khaled Madbouly, Ahmed H Hussein, Mohammed Faisal, Wagdi Elkashef, Mona Hany Emile, Marcus Edelhamre, Seon Hahn Kim, Sameh Hany Emile
    Journal of the American College of Surgeons.2022; 235(3): 482.     CrossRef
  • Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology
    Alex Dexter, Dimitrios Tsikritsis, Natalie A. Belsey, Spencer A. Thomas, Jenny Venton, Josephine Bunch, Marina Romanchikova
    Journal of Molecular Pathology.2022; 3(3): 168.     CrossRef
  • Animation Design of Multisensor Data Fusion Based on Optimized AVOD Algorithm
    Li Ding, Guobing Wei, Kai Zhang, Gengxin Sun
    Journal of Sensors.2022; 2022: 1.     CrossRef
  • Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing
    Yan Gong
    ACM Transactions on Asian and Low-Resource Language Information Processing.2022;[Epub]     CrossRef
  • Analysis and Estimation of Pathological Data and Findings with Deep Learning Methods
    Ahmet Anıl ŞAKIR, Ali Hakan IŞIK, Özlem ÖZMEN, Volkan İPEK
    Veterinary Journal of Mehmet Akif Ersoy University.2022; 7(3): 175.     CrossRef
  • Artificial Intelligence in Pathology: Friend or Enemy?
    Selim Sevim, Ezgi Dicle Serbes, Murat Bahadır, Mustafa Said Kartal, Serpil Dizbay Sak
    Journal of Ankara University Faculty of Medicine.2022; 75(1): 13.     CrossRef
  • Evaluation Challenges in the Validation of B7-H3 as Oral Tongue Cancer Prognosticator
    Meri Sieviläinen, Anna Maria Wirsing, Aini Hyytiäinen, Rabeia Almahmoudi, Priscila Rodrigues, Inger-Heidi Bjerkli, Pirjo Åström, Sanna Toppila-Salmi, Timo Paavonen, Ricardo D. Coletta, Elin Hadler-Olsen, Tuula Salo, Ahmed Al-Samadi
    Head and Neck Pathology.2021; 15(2): 469.     CrossRef
  • Amsterdam International Consensus Meeting: tumor response scoring in the pathology assessment of resected pancreatic cancer after neoadjuvant therapy
    Boris V. Janssen, Faik Tutucu, Stijn van Roessel, Volkan Adsay, Olca Basturk, Fiona Campbell, Claudio Doglioni, Irene Esposito, Roger Feakins, Noriyoshi Fukushima, Anthony J. Gill, Ralph H. Hruban, Jeffrey Kaplan, Bas Groot Koerkamp, Seung-Mo Hong, Alyssa
    Modern Pathology.2021; 34(1): 4.     CrossRef
  • Fabrication of ultra-thin 2D covalent organic framework nanosheets and their application in functional electronic devices
    Weikang Wang, Weiwei Zhao, Haotian Xu, Shujuan Liu, Wei Huang, Qiang Zhao
    Coordination Chemistry Reviews.2021; 429: 213616.     CrossRef
  • Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
    Hyun-Jong Jang, In Hye Song, Sung Hak Lee
    Applied Sciences.2021; 11(2): 808.     CrossRef
  • Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center
    Peter J Schüffler, Luke Geneslaw, D Vijay K Yarlagadda, Matthew G Hanna, Jennifer Samboy, Evangelos Stamelos, Chad Vanderbilt, John Philip, Marc-Henri Jean, Lorraine Corsale, Allyne Manzo, Neeraj H G Paramasivam, John S Ziegler, Jianjiong Gao, Juan C Peri
    Journal of the American Medical Informatics Association.2021; 28(9): 1874.     CrossRef
  • Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis
    Julia Moran-Sanchez, Antonio Santisteban-Espejo, Miguel Angel Martin-Piedra, Jose Perez-Requena, Marcial Garcia-Rojo
    Biomolecules.2021; 11(6): 793.     CrossRef
  • Development and operation of a digital platform for sharing pathology image data
    Yunsook Kang, Yoo Jung Kim, Seongkeun Park, Gun Ro, Choyeon Hong, Hyungjoon Jang, Sungduk Cho, Won Jae Hong, Dong Un Kang, Jonghoon Chun, Kyoungbun Lee, Gyeong Hoon Kang, Kyoung Chul Moon, Gheeyoung Choe, Kyu Sang Lee, Jeong Hwan Park, Won-Ki Jeong, Se Yo
    BMC Medical Informatics and Decision Making.2021;[Epub]     CrossRef
  • Sliding window based deep ensemble system for breast cancer classification
    Amin Alqudah, Ali Mohammad Alqudah
    Journal of Medical Engineering & Technology.2021; 45(4): 313.     CrossRef
  • Artificial intelligence and computational pathology
    Miao Cui, David Y. Zhang
    Laboratory Investigation.2021; 101(4): 412.     CrossRef
  • Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models
    Elham Vali-Betts, Kevin J. Krause, Alanna Dubrovsky, Kristin Olson, John Paul Graff, Anupam Mitra, Ananya Datta-Mitra, Kenneth Beck, Aristotelis Tsirigos, Cynthia Loomis, Antonio Galvao Neto, Esther Adler, Hooman H. Rashidi
    Journal of Pathology Informatics.2021; 12(1): 5.     CrossRef
  • Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancer
    Sung Hak Lee, In Hye Song, Hyun‐Jong Jang
    International Journal of Cancer.2021; 149(3): 728.     CrossRef
  • Artificial intelligence in healthcare
    Yamini D Shah, Shailvi M Soni, Manish P Patel
    Indian Journal of Pharmacy and Pharmacology.2021; 8(2): 102.     CrossRef
  • Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis
    Agathe Bédard, Thomas Westerling-Bui, Aleksandra Zuraw
    Toxicologic Pathology.2021; 49(4): 897.     CrossRef
  • An empirical analysis of machine learning frameworks for digital pathology in medical science
    S.K.B. Sangeetha, R Dhaya, Dhruv T Shah, R Dharanidharan, K. Praneeth Sai Reddy
    Journal of Physics: Conference Series.2021; 1767(1): 012031.     CrossRef
  • Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology
    Daniel Royston, Adam J. Mead, Bethan Psaila
    Hematology/Oncology Clinics of North America.2021; 35(2): 279.     CrossRef
  • Idiosyncratic Drug-Induced Liver Injury (DILI) and Herb-Induced Liver Injury (HILI): Diagnostic Algorithm Based on the Quantitative Roussel Uclaf Causality Assessment Method (RUCAM)
    Rolf Teschke, Gaby Danan
    Diagnostics.2021; 11(3): 458.     CrossRef
  • Searching Images for Consensus
    Hamid R. Tizhoosh, Phedias Diamandis, Clinton J.V. Campbell, Amir Safarpoor, Shivam Kalra, Danial Maleki, Abtin Riasatian, Morteza Babaie
    The American Journal of Pathology.2021; 191(10): 1702.     CrossRef
  • Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network
    Yao Yao, Shuiping Gou, Ru Tian, Xiangrong Zhang, Shuixiang He, Zhiguo Zhou
    BioMed Research International.2021; 2021: 1.     CrossRef
  • Artificial intelligence and sleep: Advancing sleep medicine
    Nathaniel F. Watson, Christopher R. Fernandez
    Sleep Medicine Reviews.2021; 59: 101512.     CrossRef
  • Prospective Of Artificial Intelligence: Emerging Trends In Modern Biosciences Research
    Pradeep Kumar, Ajit Kumar Singh Yadav, Abhishek Singh
    IOP Conference Series: Materials Science and Engineering.2021; 1020(1): 012008.     CrossRef
  • Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives
    Simon Lennartz, Thomas Dratsch, David Zopfs, Thorsten Persigehl, David Maintz, Nils Große Hokamp, Daniel Pinto dos Santos
    Journal of Medical Internet Research.2021; 23(2): e24221.     CrossRef
  • HEAL: an automated deep learning framework for cancer histopathology image analysis
    Yanan Wang, Nicolas Coudray, Yun Zhao, Fuyi Li, Changyuan Hu, Yao-Zhong Zhang, Seiya Imoto, Aristotelis Tsirigos, Geoffrey I Webb, Roger J Daly, Jiangning Song, Zhiyong Lu
    Bioinformatics.2021; 37(22): 4291.     CrossRef
  • A Review of Applications of Artificial Intelligence in Gastroenterology
    Khalid Nawab, Ravi Athwani, Awais Naeem, Muhammad Hamayun, Momna Wazir
    Cureus.2021;[Epub]     CrossRef
  • Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review
    Xiaoliang Xie, Xulin Wang, Yuebin Liang, Jingya Yang, Yan Wu, Li Li, Xin Sun, Pingping Bing, Binsheng He, Geng Tian, Xiaoli Shi
    Frontiers in Oncology.2021;[Epub]     CrossRef
  • Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
    Hyeongsub Kim, Hongjoon Yoon, Nishant Thakur, Gyoyeon Hwang, Eun Jung Lee, Chulhong Kim, Yosep Chong
    Scientific Reports.2021;[Epub]     CrossRef
  • Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study
    Veronika Shavlokhova, Sameena Sandhu, Christa Flechtenmacher, Istvan Koveshazi, Florian Neumeier, Víctor Padrón-Laso, Žan Jonke, Babak Saravi, Michael Vollmer, Andreas Vollmer, Jürgen Hoffmann, Michael Engel, Oliver Ristow, Christian Freudlsperger
    Journal of Clinical Medicine.2021; 10(22): 5326.     CrossRef
  • A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
    Sarah N. Dudgeon, Si Wen, Matthew G. Hanna, Rajarsi Gupta, Mohamed Amgad, Manasi Sheth, Hetal Marble, Richard Huang, Markus D. Herrmann, Clifford H. Szu, Darick Tong, Bruce Werness, Evan Szu, Denis Larsimont, Anant Madabhushi, Evangelos Hytopoulos, Weijie
    Journal of Pathology Informatics.2021; 12(1): 45.     CrossRef
  • Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students' Perception
    Sotirios Bisdas, Constantin-Cristian Topriceanu, Zosia Zakrzewska, Alexandra-Valentina Irimia, Loizos Shakallis, Jithu Subhash, Maria-Madalina Casapu, Jose Leon-Rojas, Daniel Pinto dos Santos, Dilys Miriam Andrews, Claudia Zeicu, Ahmad Mohammad Bouhuwaish
    Frontiers in Public Health.2021;[Epub]     CrossRef
  • Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review
    Robert Y. Osamura, Naruaki Matsui, Masato Kawashima, Hiroyasu Saiga, Maki Ogura, Tomoharu Kiyuna
    Acta Cytologica.2021; 65(4): 342.     CrossRef
  • Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging
    Frederik Großerueschkamp, Hendrik Jütte, Klaus Gerwert, Andrea Tannapfel
    Visceral Medicine.2021; 37(6): 482.     CrossRef
  • Feasibility of fully automated classification of whole slide images based on deep learning
    Kyung-Ok Cho, Sung Hak Lee, Hyun-Jong Jang
    The Korean Journal of Physiology & Pharmacology.2020; 24(1): 89.     CrossRef
  • Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations
    Joshua Kubach, Angelika Muhlebner‐Fahrngruber, Figen Soylemezoglu, Hajime Miyata, Pitt Niehusmann, Mrinalini Honavar, Fabio Rogerio, Se‐Hoon Kim, Eleonora Aronica, Rita Garbelli, Samuel Vilz, Alexander Popp, Stefan Walcher, Christoph Neuner, Michael Schol
    Epilepsia.2020; 61(3): 421.     CrossRef
  • Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches
    Tahsin Kurc, Spyridon Bakas, Xuhua Ren, Aditya Bagari, Alexandre Momeni, Yue Huang, Lichi Zhang, Ashish Kumar, Marc Thibault, Qi Qi, Qian Wang, Avinash Kori, Olivier Gevaert, Yunlong Zhang, Dinggang Shen, Mahendra Khened, Xinghao Ding, Ganapathy Krishnamu
    Frontiers in Neuroscience.2020;[Epub]     CrossRef
  • Artificial intelligence as the next step towards precision pathology
    B. Acs, M. Rantalainen, J. Hartman
    Journal of Internal Medicine.2020; 288(1): 62.     CrossRef
  • Introduction to digital pathology and computer-aided pathology
    Soojeong Nam, Yosep Chong, Chan Kwon Jung, Tae-Yeong Kwak, Ji Youl Lee, Jihwan Park, Mi Jung Rho, Heounjeong Go
    Journal of Pathology and Translational Medicine.2020; 54(2): 125.     CrossRef
  • Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
    Zeeshan Ahmed, Khalid Mohamed, Saman Zeeshan, XinQi Dong
    Database.2020;[Epub]     CrossRef
  • Scoring pleurisy in slaughtered pigs using convolutional neural networks
    Abigail R. Trachtman, Luca Bergamini, Andrea Palazzi, Angelo Porrello, Andrea Capobianco Dondona, Ercole Del Negro, Andrea Paolini, Giorgio Vignola, Simone Calderara, Giuseppe Marruchella
    Veterinary Research.2020;[Epub]     CrossRef
  • Current Status of Computational Intelligence Applications in Dermatological Clinical Practice
    Carmen Rodríguez-Cerdeira, José Luís González-Cespón, Roberto Arenas
    The Open Dermatology Journal.2020; 14(1): 6.     CrossRef
  • New unified insights on deep learning in radiological and pathological images: Beyond quantitative performances to qualitative interpretation
    Yoichi Hayashi
    Informatics in Medicine Unlocked.2020; 19: 100329.     CrossRef
  • Artificial Intelligence in Cardiology: Present and Future
    Francisco Lopez-Jimenez, Zachi Attia, Adelaide M. Arruda-Olson, Rickey Carter, Panithaya Chareonthaitawee, Hayan Jouni, Suraj Kapa, Amir Lerman, Christina Luong, Jose R. Medina-Inojosa, Peter A. Noseworthy, Patricia A. Pellikka, Margaret M. Redfield, Vero
    Mayo Clinic Proceedings.2020; 95(5): 1015.     CrossRef
  • Artificial intelligence in oncology
    Hideyuki Shimizu, Keiichi I. Nakayama
    Cancer Science.2020; 111(5): 1452.     CrossRef
  • Artificial intelligence and the future of global health
    Nina Schwalbe, Brian Wahl
    The Lancet.2020; 395(10236): 1579.     CrossRef
  • The future of pathology is digital
    J.D. Pallua, A. Brunner, B. Zelger, M. Schirmer, J. Haybaeck
    Pathology - Research and Practice.2020; 216(9): 153040.     CrossRef
  • Weakly-supervised learning for lung carcinoma classification using deep learning
    Fahdi Kanavati, Gouji Toyokawa, Seiya Momosaki, Michael Rambeau, Yuka Kozuma, Fumihiro Shoji, Koji Yamazaki, Sadanori Takeo, Osamu Iizuka, Masayuki Tsuneki
    Scientific Reports.2020;[Epub]     CrossRef
  • The use of artificial intelligence, machine learning and deep learning in oncologic histopathology
    Ahmed S. Sultan, Mohamed A. Elgharib, Tiffany Tavares, Maryam Jessri, John R. Basile
    Journal of Oral Pathology & Medicine.2020; 49(9): 849.     CrossRef
  • Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions
    Anil V. Parwani, Mahul B. Amin
    Advances in Anatomic Pathology.2020; 27(4): 221.     CrossRef
  • Advances in tissue-based imaging: impact on oncology research and clinical practice
    Arman Rahman, Chowdhury Jahangir, Seodhna M. Lynch, Nebras Alattar, Claudia Aura, Niamh Russell, Fiona Lanigan, William M. Gallagher
    Expert Review of Molecular Diagnostics.2020; 20(10): 1027.     CrossRef
  • Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review
    Nishant Thakur, Hongjun Yoon, Yosep Chong
    Cancers.2020; 12(7): 1884.     CrossRef
  • Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit
    Farah Deshmukh, Shamel S. Merchant
    American Journal of Gastroenterology.2020; 115(10): 1657.     CrossRef
  • Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning
    Hyun-Jong Jang, Ahwon Lee, J Kang, In Hye Song, Sung Hak Lee
    World Journal of Gastroenterology.2020; 26(40): 6207.     CrossRef
  • Application of system analysis methods for modeling the development of hand-arm vibration syndrome: problems and approaches to solution
    M P Diakovich, M V Krivov
    Journal of Physics: Conference Series.2020; 1661(1): 012029.     CrossRef
  • Histo-ELISA technique for quantification and localization of tissue components
    Zhongmin Li, Silvia Goebel, Andreas Reimann, Martin Ungerer
    Scientific Reports.2020;[Epub]     CrossRef
  • Role of artificial intelligence in diagnostic oral pathology-A modern approach
    AyinampudiBhargavi Krishna, Azra Tanveer, PanchaVenkat Bhagirath, Ashalata Gannepalli
    Journal of Oral and Maxillofacial Pathology.2020; 24(1): 152.     CrossRef
  • Applications of deep learning for the analysis of medical data
    Hyun-Jong Jang, Kyung-Ok Cho
    Archives of Pharmacal Research.2019; 42(6): 492.     CrossRef
  • PROMISE CLIP Project: A Retrospective, Multicenter Study for Prostate Cancer that Integrates Clinical, Imaging and Pathology Data
    Jihwan Park, Mi Jung Rho, Yong Hyun Park, Chan Kwon Jung, Yosep Chong, Choung-Soo Kim, Heounjeong Go, Seong Soo Jeon, Minyong Kang, Hak Jong Lee, Sung Il Hwang, Ji Youl Lee
    Applied Sciences.2019; 9(15): 2982.     CrossRef
  • Key challenges for delivering clinical impact with artificial intelligence
    Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, Dominic King
    BMC Medicine.2019;[Epub]     CrossRef
  • Deep Learning for Whole Slide Image Analysis: An Overview
    Neofytos Dimitriou, Ognjen Arandjelović, Peter D. Caie
    Frontiers in Medicine.2019;[Epub]     CrossRef
  • Barriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review
    Mir Mohammed Assadullah
    SSRN Electronic Journal .2019;[Epub]     CrossRef
Original Articles
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
  • 5,801 View
  • 116 Download
  • 5 Web of Science
  • 5 Crossref
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.

Citations

Citations to this article as recorded by  
  • The human myocardium harbors a population of naive B-cells with a distinctive gene expression signature conserved across species
    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
Loss of Nuclear BAP1 Expression Is Associated with High WHO/ISUP Grade in Clear Cell Renal Cell Carcinoma
Young Chan Wi, Ahrim Moon, Min Jung Jung, Yeseul Kim, Seong Sik Bang, Kiseok Jang, Seung Sam Paik, Su-Jin Shin
J Pathol Transl Med. 2018;52(6):378-385.   Published online October 1, 2018
DOI: https://doi.org/10.4132/jptm.2018.09.21
  • 7,062 View
  • 192 Download
  • 11 Web of Science
  • 12 Crossref
AbstractAbstract PDF
Background
BRCA1-associated protein 1 (BAP1) mutations are frequently reported in clear cell renal cell carcinoma (ccRCC); however, very few studies have evaluated the role of these mutations in other renal cell carcinoma (RCC) subtypes. Therefore, we analyzed BAP1 protein expression using immunohistochemistry in several RCC subtypes and assessed its relationship with clinicopathological characteristics of patients.
Methods
BAP1 expression was immunohistochemically evaluated in tissue microarray blocks constructed from 371 samples of RCC collected from two medical institutions. BAP1 expression was evaluated based on the extent of nuclear staining in tumor cells, and no expression or expression in < 10% of tumor cells was defined as negative.
Results
Loss of BAP1 expression was observed in ccRCC (56/300, 18.7%), chromophobe RCC (6/26, 23.1%), and clear cell papillary RCC (1/4, 25%), while we failed to detect BAP1 expression loss in papillary RCC, acquired cystic disease-associated RCC, or collecting duct carcinoma. In ccRCC, loss of BAP1 expression was significantly associated with high World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grade (p = .002); however, no significant correlation was observed between loss of BAP1 expression and survival in ccRCC. Loss of BAP1 expression showed no association with prognostic factors in chromophobe RCC.
Conclusions
Loss of BAP1 nuclear expression was observed in both ccRCC and chromophobe RCC. In addition, BAP1 expression loss was associated with poor prognostic factors such as high WHO/ISUP grade in ccRCC.

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  • Immune regulation and prognosis indicating ability of a newly constructed multi-genes containing signature in clear cell renal cell carcinoma
    Ziwei Gui, Juan Du, Nan Wu, Ningning Shen, Zhiqing Yang, Huijun Yang, Xuzhi Wang, Na Zhao, Zixin Zeng, Rong Wei, Wenxia Ma, Chen Wang
    BMC Cancer.2023;[Epub]     CrossRef
  • Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study
    Derek H Liu, Komal A Dani, Sharath S Reddy, Xiaomeng Lei, Natalie L Demirjian, Darryl H Hwang, Bino A Varghese, Suhn Kyong Rhie, Felix Y. Yap, David I. Quinn, Imran Siddiqi, Manju Aron, Ulka Vaishampayan, Haris Zahoor, Steven Y Cen, Inderbir S Gill, Vinay
    Oncology.2023; 101(6): 375.     CrossRef
  • Immunohistochemistry for the diagnosis of renal epithelial neoplasms
    Mahmut Akgul, Sean R Williamson
    Seminars in Diagnostic Pathology.2022; 39(1): 1.     CrossRef
  • BRCA1-Associated Protein 1 (BAP-1) as a Prognostic and Predictive Biomarker in Clear Cell Renal Cell Carcinoma: A Systematic Review
    Shuchi Gulati, Melissa Previtera, Primo N. Lara
    Kidney Cancer.2022; 6(1): 23.     CrossRef
  • Renal Cell Carcinoma in End-Stage Renal Disease: A Review and Update
    Ziad M. El-Zaatari, Luan D. Truong
    Biomedicines.2022; 10(3): 657.     CrossRef
  • CD117, BAP1, MTAP, and TdT Is a Useful Immunohistochemical Panel to Distinguish Thymoma from Thymic Carcinoma
    Mounika Angirekula, Sindy Y Chang, Sarah M. Jenkins, Patricia T. Greipp, William R. Sukov, Randolph S. Marks, Kenneth R. Olivier, Stephen D. Cassivi, Anja C Roden
    Cancers.2022; 14(9): 2299.     CrossRef
  • BAP1 in cancer: epigenetic stability and genome integrity
    Sabrina Caporali, Alessio Butera, Ivano Amelio
    Discover Oncology.2022;[Epub]     CrossRef
  • Bioinformatic analysis identifying FGF1 gene as a new prognostic indicator in clear cell Renal Cell Carcinoma
    Xiaoqin Zhang, Ziyue Wang, Zixin Zeng, Ningning Shen, Bin Wang, Yaping Zhang, Honghong Shen, Wei Lu, Rong Wei, Wenxia Ma, Chen Wang
    Cancer Cell International.2021;[Epub]     CrossRef
  • Identification of Four Pathological Stage-Relevant Genes in Association with Progression and Prognosis in Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis
    Dengyong Xu, Yuzi Xu, Yiming Lv, Fei Wu, Yunlong Liu, Ming Zhu, Dake Chen, Bingjun Bai
    BioMed Research International.2020; 2020: 1.     CrossRef
  • Functional characterisation guides classification of novel BAP1 germline variants
    Jing Han Hong, Siao Ting Chong, Po-Hsien Lee, Jing Tan, Hong Lee Heng, Nur Diana Binte Ishak, Sock Hoai Chan, Bin Tean Teh, Joanne Ngeow
    npj Genomic Medicine.2020;[Epub]     CrossRef
  • Tissue-Based Immunohistochemical Markers for Diagnosis and Classification of Renal Cell Carcinoma
    Liang G Qu, Vaisnavi Thirugnanasundralingam, Damien Bolton, Antonio Finelli, Nathan Lawrentschuk
    Société Internationale d’Urologie Journal.2020; 1(1): 68.     CrossRef
  • Radiogenomics: bridging imaging and genomics
    Zuhir Bodalal, Stefano Trebeschi, Thi Dan Linh Nguyen-Kim, Winnie Schats, Regina Beets-Tan
    Abdominal Radiology.2019; 44(6): 1960.     CrossRef
Review
Let Archived Paraffin Blocks Be Utilized for Research with Waiver of Informed Consent
Yong-Jin Kim, Jeong Sik Park, Karam Ko, Chang Rok Jeong
J Pathol Transl Med. 2018;52(3):141-147.   Published online April 5, 2018
DOI: https://doi.org/10.4132/jptm.2018.02.07
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AbstractAbstract PDF
Advances in biomedical and genetic research have contributed to more effective public health improvement via bench-to-bed research and the emergence of personalized medicine. This has certainly showcased the importance of archived human tissues, especially paraffin-embedded blocks in pathology. Currently in Korea, undue legislative regulations of the Bioethics and Safety Act suspend and at times discourage studies from taking place. In this paper, the authors underline the value of paraffin blocks in the era of personalized and translational medicine. We discuss detailed clauses regarding the applicability of paraffin blocks from a legal perspective and compare Korea’s regulations with those of other countries. The necessity for allowing waived consent and Institutional Review Board (IRB) approval will be argued throughout. The authors suggest that researchers declare the following to obtain IRB approval and waiver of informed consents: research could not be practically carried out without a waiver of consent; the proposed research presents no more than minimal risk of harm to subjects, and the waiver of consent will not adversely affect the rights and welfare of subjects; and research will not utilize a tissue block if only 1 is available for each subject, to allow future clinical use such as re-evaluation or further studies.

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  • Expression Profiles of GILZ and SGK-1 in Potentially Malignant and Malignant Human Oral Lesions
    Mahmood S. Mozaffari, Rafik Abdelsayed
    Frontiers in Oral Health.2021;[Epub]     CrossRef
  • IRB review points for studies utilizing paraffin blocks archived in the pathology laboratory
    Yong-Jin Kim, Chang Rok Jeong, Jeong Sik Park
    Yeungnam University Journal of Medicine.2018; 35(1): 36.     CrossRef
Original Article
Cytologic Diagnosis of Noninvasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features and Its Impact on the Risk of Malignancy in the Bethesda System for Reporting Thyroid Cytopathology: An Institutional Experience
Milim Kim, Joung Eun Kim, Hyun Jeong Kim, Yul Ri Chung, Yoonjin Kwak, So Yeon Park
J Pathol Transl Med. 2018;52(3):171-178.   Published online April 3, 2018
DOI: https://doi.org/10.4132/jptm.2018.04.03
  • 8,875 View
  • 196 Download
  • 23 Web of Science
  • 18 Crossref
AbstractAbstract PDF
Background
This study was performed to analyze cytologic diagnosis of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and its impact on the risk of malignancy (ROM) in the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC).
Methods
Five thousand five hundred and forty-nine cases of thyroid fine-needle aspiration cytology (FNAC) diagnosed between 2012 and 2014 were included in this study. Diagnostic categories based on TBSRTC were compared with final surgical diagnoses, and the ROM in each category was calculated both when NIFTP was included in malignant lesions and when excluded from malignant lesions.
Results
Of the 5,549 thyroid FNAC cases, 1,891 cases underwent surgical resection. In final diagnosis, 1,700 cases were revealed as papillary thyroid carcinoma (PTC), and 25 cases were reclassified as NIFTP. The cytologic diagnoses of NIFTP were non-diagnostic in one, benign in five, atypia of undetermined significance (AUS) in 14, follicular neoplasm in two, and suspicious for malignancy in three cases. Collectively, NIFTP/encapsulated follicular variant of PTC (EFVPTC) were more frequently classified as benign, AUS, or follicular neoplasm and less frequently categorized as malignant compared to conventional PTCs. Exclusion of NIFTP from malignant diagnoses resulted in a slight decrease in malignancy rates in non-diagnostic, benign, AUS, follicular neoplasm, and suspicious for malignancy categories without any statistical significance.
Conclusions
The decrease in the ROM was not significant when NIFTP was excluded from malignant lesions. In thyroid FNACs, NIFTP/EFVPTCs were mostly classified into indeterminate categories. Therefore, it might be feasible to separate NIFTP/EFVPTC from conventional PTC on FNAC to guide clinicians to conservative management for patients with NIFTP/EFVPTC.

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  • Spatial transcriptomics reveals prognosis‐associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment
    Kai Yan, Qing‐Zhi Liu, Rong‐Rong Huang, Yi‐Hua Jiang, Zhen‐Hua Bian, Si‐Jin Li, Liang Li, Fei Shen, Koichi Tsuneyama, Qing‐Ling Zhang, Zhe‐Xiong Lian, Haixia Guan, Bo Xu
    Clinical and Translational Medicine.2024;[Epub]     CrossRef
  • Cytological features of “Non-invasive follicular tumour with papillary like nuclear features” – A single institutional experience in India
    K Amita, HB Rakshitha, M Sanjay, Prashantha Kalappa
    Journal of Cytology.2023; 40(1): 28.     CrossRef
  • Detailed fine needle aspiration cytopathology findings of noninvasive follicular thyroid neoplasm with papillary‐like nuclear features with nuclear grading correlated to that of biopsy and Bethesda category and systematic review
    Sevgiye Kaçar Özkara, Gupse Turan
    Diagnostic Cytopathology.2023; 51(12): 758.     CrossRef
  • Non-Invasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features Is Not a Cytological Diagnosis, but It Influences Cytological Diagnosis Outcomes: A Systematic Review and Meta-Analysis
    Elina Haaga, David Kalfert, Marie Ludvíková, Ivana Kholová
    Acta Cytologica.2022; 66(2): 85.     CrossRef
  • Noninvasive follicular thyroid neoplasm with papillary-like nuclear features: its updated diagnostic criteria, preoperative cytologic diagnoses and impact on the risk of malignancy
    Hee Young Na, So Yeon Park
    Journal of Pathology and Translational Medicine.2022; 56(6): 319.     CrossRef
  • Usage and Diagnostic Yield of Fine-Needle Aspiration Cytology and Core Needle Biopsy in Thyroid Nodules: A Systematic Review and Meta-Analysis of Literature Published by Korean Authors
    Soon-Hyun Ahn
    Clinical and Experimental Otorhinolaryngology.2021; 14(1): 116.     CrossRef
  • Comprehensive DNA Methylation Profiling Identifies Novel Diagnostic Biomarkers for Thyroid Cancer
    Jong-Lyul Park, Sora Jeon, Eun-Hye Seo, Dong Hyuck Bae, Young Mun Jeong, Yourha Kim, Ja Seong Bae, Seon-Kyu Kim, Chan Kwon Jung, Yong Sung Kim
    Thyroid.2020; 30(2): 192.     CrossRef
  • Differences in surgical resection rate and risk of malignancy in thyroid cytopathology practice between Western and Asian countries: A systematic review and meta‐analysis
    Huy Gia Vuong, Hanh Thi Tuyet Ngo, Andrey Bychkov, Chan Kwon Jung, Trang Huyen Vu, Kim Bach Lu, Kennichi Kakudo, Tetsuo Kondo
    Cancer Cytopathology.2020; 128(4): 238.     CrossRef
  • Noninvasive follicular neoplasm with papillary like nuclear features: A comprehensive analysis with a diagnostic algorithm
    Chanchal Rana, Shreyamsa Manjunath, Pooja Ramakant, Kulranjan Singh, Suresh Babu, Anand Mishra
    Diagnostic Cytopathology.2020; 48(4): 330.     CrossRef
  • Noninvasive follicular thyroid neoplasm with papillary‐like nuclear features and the risk of malignancy in The Bethesda System for the Reporting of Thyroid Cytopathology
    Danielle Elliott Range, Xiaoyin “Sara” Jiang
    Diagnostic Cytopathology.2020; 48(6): 531.     CrossRef
  • Did Introducing a New Category of Thyroid Tumors (Non-invasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features) Decrease the Risk of Malignancy for the Diagnostic Categories in the Bethesda System for Reporting Thyroid Cytopathology?
    Janusz Kopczyński, Agnieszka Suligowska, Kornelia Niemyska, Iwona Pałyga, Agnieszka Walczyk, Danuta Gąsior-Perczak, Artur Kowalik, Kinga Hińcza, Ryszard Mężyk, Stanisław Góźdź, Aldona Kowalska
    Endocrine Pathology.2020; 31(2): 143.     CrossRef
  • High risk of malignancy in cases with atypia of undetermined significance on fine needle aspiration of thyroid nodules even after exclusion of NIFTP
    Sevgiye Kaçar Özkara, Büşra Yaprak Bayrak, Gupse Turan
    Diagnostic Cytopathology.2020; 48(11): 986.     CrossRef
  • The importance of risk of neoplasm as an outcome in cytologic‐histologic correlation studies on thyroid fine needle aspiration
    Yu‐Hsin Chen, Kristen L. Partyka, Rae Dougherty, Harvey M. Cramer, Howard H. Wu
    Diagnostic Cytopathology.2020; 48(12): 1237.     CrossRef
  • Preoperative diagnostic categories of fine needle aspiration cytology for histologically proven thyroid follicular adenoma and carcinoma, and Hurthle cell adenoma and carcinoma: Analysis of cause of under- or misdiagnoses
    Hee Young Na, Jae Hoon Moon, June Young Choi, Hyeong Won Yu, Woo-Jin Jeong, Yeo Koon Kim, Ji-Young Choe, So Yeon Park, Paula Soares
    PLOS ONE.2020; 15(11): e0241597.     CrossRef
  • How is noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) shaping the way we interpret thyroid cytology?
    Michiya Nishino
    Journal of the American Society of Cytopathology.2019; 8(1): 1.     CrossRef
  • Cytological Diagnoses Associated with Noninvasive Follicular Thyroid Neoplasms with Papillary-Like Nuclear Features According to the Bethesda System for Reporting Thyroid Cytopathology: A Systematic Review and Meta-Analysis
    Massimo Bongiovanni, Luca Giovanella, Francesco Romanelli, Pierpaolo Trimboli
    Thyroid.2019; 29(2): 222.     CrossRef
  • Preoperative Diagnostic Categories of Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features in Thyroid Core Needle Biopsy and Its Impact on Risk of Malignancy
    Hee Young Na, Ji Won Woo, Jae Hoon Moon, June Young Choi, Woo-Jin Jeong, Yeo Koon Kim, Ji-Young Choe, So Yeon Park
    Endocrine Pathology.2019; 30(4): 329.     CrossRef
  • Papillary Thyroid Microcarcinoma: Reclassification to Non-Invasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features (NIFTP): a Retrospective Clinicopathologic Study
    Khurram Shafique, Virginia A. LiVolsi, Kathleen Montone, Zubair W. Baloch
    Endocrine Pathology.2018; 29(4): 339.     CrossRef

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