Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy

• Published in: PloS one Vol. 15; no. 4; p. e0231880
• Main Authors: Zhou, Guanyu, Xiao, Xun, Tu, Mengtian, Liu, Peixi, Yang, Dan, Liu, Xiaogang, Zhang, Renyi, Li, Liangping, Lei, Shan, Wang, Han, Song, Yan, Wang, Pu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.04.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; CAI; Colon; Colonoscopy; Colorectal cancer; Computer and Information Sciences; Computer assisted instruction; Datasets; Endoscopy; Gastroenterology; Histology; Image detection; Lesions; Machine learning; Malignancy; Medicine and Health Sciences; Morphology; Polyps; Sensitivity; Studies; Subgroups; Tumors; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0231880

Evidence has shown that deep learning computer aided detection (CADe) system achieved high overall detection accuracy for polyp detection during colonoscopy. The detection performance of CADe system on non-polypoid laterally spreading tumors (LSTs) and sessile serrated adenomas/polyps (SSA/Ps), with higher risk for malignancy transformation and miss rate, has not been exclusively investigated. A previously validated deep learning CADe system for polyp detection was tested exclusively on LSTs and SSA/Ps. 1451 LST images from 184 patients were collected between July 2015 and January 2019, 82 SSA/Ps videos from 26 patients were collected between September 2018 and January 2019. The per-frame sensitivity and per-lesion sensitivity were calculated. (1) For LSTs image dataset, the system achieved an overall per-image sensitivity and per-lesion sensitivity of 94.07% (1365/1451) and 98.99% (197/199) respectively. The per-frame sensitivity for LST-G(H), LST-G(M), LST-NG(F), LST-NG(PD) was 93.97% (343/365), 98.72% (692/701), 85.71% (324/378) and 85.71% (6/7) respectively. The per-lesion sensitivity of each subgroup was 100.00% (71/71), 100.00% (64/64), 98.31% (58/59) and 80.00% (4/5). (2) For SSA/Ps video dataset, the system achieved an overall per-frame sensitivity and per-lesion sensitivity of 84.10% (15883/18885) and 100.00% (42/42), respectively. This study demonstrated that a local-feature-prioritized automatic CADe system could detect LSTs and SSA/Ps with high sensitivity. The per-frame sensitivity for non-granular LSTs and small SSA/Ps should be further improved.