A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies
- Title
- A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies
- Author
- 명재경
- Issue Date
- 2021-02
- Publisher
- AMER ASSOC CANCER RESEARCH
- Citation
- CLINICAL CANCER RESEARCH, v. 27, no. 3, page. 719-728
- 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
- URI
- https://aacrjournals.org/clincancerres/article/27/3/719/83404/A-Prospective-Validation-and-Observer-Performancehttps://repository.hanyang.ac.kr/handle/20.500.11754/176332
- ISSN
- 1078-0432; 1557-3265
- DOI
- 10.1158/1078-0432.CCR-20-3159
- Appears in Collections:
- COLLEGE OF MEDICINE[S](의과대학) > MEDICINE(의학과) > Articles
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