A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jeonghyuk | - |
dc.contributor.author | Jang, Bo Gun | - |
dc.contributor.author | Kim, Yeong Won | - |
dc.contributor.author | Park, Hyunho | - |
dc.contributor.author | Kim, Baek-Hui | - |
dc.contributor.author | Kim, Myeung Ju | - |
dc.contributor.author | Ko, Hyungsuk | - |
dc.contributor.author | Gwak, Jae Moon | - |
dc.contributor.author | Lee, Eun Ji | - |
dc.contributor.author | Chung, Yul Ri | - |
dc.contributor.author | Kim, Kyungdoc | - |
dc.contributor.author | Myung, Jae Kyung | - |
dc.contributor.author | Park, Jeong Hwan | - |
dc.contributor.author | Choi, Dong Youl | - |
dc.contributor.author | Jung, Chang Won | - |
dc.contributor.author | Park, Bong-Hee | - |
dc.contributor.author | Jung, Kyu-Hwan | - |
dc.contributor.author | Kim, Dong-Il | - |
dc.date.accessioned | 2022-07-07T00:59:58Z | - |
dc.date.available | 2022-07-07T00:59:58Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 1078-0432 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142406 | - |
dc.description.abstract | Purpose: Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are continuously increasing, and there is a need for an automatic computerized diagnostic system to reduce the diagnostic burden. In this study, we developed an algorithm to classify gastric epithelial tumors automatically and assessed its performance in a large series of gastric biopsies and its benefits as an assistance tool. Experimental Design: Using 2,434 whole-slide images, we developed an algorithm based on convolutional neural networks to classify a gastric biopsy image into one of three categories: negative for dysplasia (NFD), tubular adenoma, or carcinoma. The performance of the algorithm was evaluated by using 7,440 biopsy specimens collected prospectively. The impact of algorithm-assisted diagnosis was assessed by six pathologists using 150 gastric biopsy cases. Results: Diagnostic performance evaluated by the AUROC curve in the prospective study was 0.9790 for two-tier classification: negative (NFD) versus positive (all cases except NFD). When limited to epithelial tumors, the sensitivity and specificity were 1.000 and 0.9749. Algorithm-assisted digital image viewer (DV) resulted in 47% reduction in review time per image compared with DV only and 58% decrease to microscopy. Conclusions: Our algorithm has demonstrated high accuracy in classifying epithelial tumors and its benefits as an assistance tool, which can serve as a potential screening aid system in diagnosing gastric biopsy specimens. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | AMER ASSOC CANCER RESEARCH | - |
dc.title | A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Myung, Jae Kyung | - |
dc.identifier.doi | 10.1158/1078-0432.CCR-20-3159 | - |
dc.identifier.scopusid | 2-s2.0-85100419662 | - |
dc.identifier.wosid | 000617323900008 | - |
dc.identifier.bibliographicCitation | CLINICAL CANCER RESEARCH, v.27, no.3, pp.719 - 728 | - |
dc.relation.isPartOf | CLINICAL CANCER RESEARCH | - |
dc.citation.title | CLINICAL CANCER RESEARCH | - |
dc.citation.volume | 27 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 719 | - |
dc.citation.endPage | 728 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Oncology | - |
dc.relation.journalWebOfScienceCategory | Oncology | - |
dc.subject.keywordPlus | WHOLE-SLIDE IMAGES | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | CANCER | - |
dc.subject.keywordPlus | JAPAN | - |
dc.identifier.url | https://clincancerres.aacrjournals.org/content/27/3/719 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.