Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 명재경 | - |
dc.date.accessioned | 2022-11-07T05:17:55Z | - |
dc.date.available | 2022-11-07T05:17:55Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | CLINICAL CANCER RESEARCH, v. 27, no. 3, page. 719-728 | en_US |
dc.identifier.issn | 1078-0432; 1557-3265 | en_US |
dc.identifier.uri | https://aacrjournals.org/clincancerres/article/27/3/719/83404/A-Prospective-Validation-and-Observer-Performance | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/176332 | - |
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.Y | en_US |
dc.description.sponsorship | This study was supported by Green Cross Laboratory and VUNO Inc. | en_US |
dc.language | en | en_US |
dc.publisher | AMER ASSOC CANCER RESEARCH | en_US |
dc.title | A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies | en_US |
dc.type | Article | en_US |
dc.relation.no | 3 | - |
dc.relation.volume | 27 | - |
dc.identifier.doi | 10.1158/1078-0432.CCR-20-3159 | en_US |
dc.relation.page | 719-728 | - |
dc.relation.journal | CLINICAL CANCER RESEARCH | - |
dc.contributor.googleauthor | Park, Jeonghyuk | - |
dc.contributor.googleauthor | Jang, Bo Gun | - |
dc.contributor.googleauthor | Kim, Yeong Won | - |
dc.contributor.googleauthor | Park, Hyunho | - |
dc.contributor.googleauthor | Kim, Baek-Hui | - |
dc.contributor.googleauthor | Kim, Myeung Ju | - |
dc.contributor.googleauthor | Ko, Hyungsuk | - |
dc.contributor.googleauthor | Gwak, Jae Moon | - |
dc.contributor.googleauthor | Lee, Eun Ji | - |
dc.contributor.googleauthor | Myung, Jae Kyung | - |
dc.relation.code | 2021008195 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF MEDICINE[S] | - |
dc.sector.department | DEPARTMENT OF MEDICINE | - |
dc.identifier.pid | tontos | - |
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