A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies
- Authors
- Park, Jeonghyuk; Jang, Bo Gun; Kim, Yeong Won; Park, Hyunho; Kim, Baek-Hui; Kim, Myeung Ju; Ko, Hyungsuk; Gwak, Jae Moon; Lee, Eun Ji; Chung, Yul Ri; Kim, Kyungdoc; Myung, Jae Kyung; Park, Jeong Hwan; Choi, Dong Youl; Jung, Chang Won; Park, Bong-Hee; Jung, Kyu-Hwan; Kim, Dong-Il
- Issue Date
- Feb-2021
- Publisher
- AMER ASSOC CANCER RESEARCH
- Citation
- CLINICAL CANCER RESEARCH, v.27, no.3, pp.719 - 728
- Indexed
- SCIE
SCOPUS
- Journal Title
- CLINICAL CANCER RESEARCH
- Volume
- 27
- Number
- 3
- Start Page
- 719
- End Page
- 728
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142406
- DOI
- 10.1158/1078-0432.CCR-20-3159
- ISSN
- 1078-0432
- 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.
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