In Gastrointestinal endoscopy ; h5-index 72.0
BACKGROUND AND AIMS : Differentiation of colorectal cancers with deep submucosal invasion (T1b) from colorectal cancers with superficial invasion (T1a) or no invasion (Tis) is not straightforward. This study aimed to develop a computer aided diagnosis system (CADx) to establish the diagnosis of early-stage cancers using non-magnified endoscopic white light images alone.
METHODS : A total of 1513 lesions (Tis 1074, T1a 145, T1b 294) in 5108 images were collected from 1470 patients at ten academic hospitals and assigned to training and testing datasets (3:1). The ResNet-50 network was used as the backbone to extract features from images. Over sampling and focal loss were used to compensate class imbalance of invasive stage. Diagnostic performance was assessed using the testing dataset including 403 CRCs with 1392 images. Two experts and two trainees read the identical testing dataset.
RESULTS : At 90% cutoff for per lesion score, CADx showed the highest specificity of 94.4% [95% confidence interval: 91.3 - 96.6], with 59.8% [48.3 - 70.4] sensitivity and 87.3% [83.7 - 90.4] accuracy. The area under the characteristic curve was 85.1% [79.9 - 90.4] for CADx, 88.2% [83.7 - 92.8] for expert 1, 85.9% [80.9 - 90.9] for expert 2, 77.0% [71.5 - 82.4] for trainee 1 (vs. CADx: p=0.0076), and 66.2% [60.6 - 71.9] for trainee 2 (p<0.0001). The function was also confirmed on nine short videos.
CONCLUSION : CADx developed with endoscopic white light images showed excellent per lesion specificity and accuracy for T1b lesion diagnosis, equivalent to experts and superior to trainees. (UMIN000037053) (249 =<250 words).
Nemoto Daiki, Guo Zhe, Katsuki Shinichi, Takezawa Takahito, Maemoto Ryo, Kawasaki Keisuke, Inoue Ken, Akutagawa Takashi, Tanaka Hirohito, Sato Koichiro, Omori Teppei, Takanashi Kunihiro, Hayashi Yoshikazu, Nakajima Yuki, Miyakura Yasuyuki, Matsumoto Takayuki, Yoshida Naohisa, Esaki Motohiro, Uraoka Toshio, Kato Hiroyuki, Inoue Yuji, Peng Boyuan, Zhang Ruiyao, Hisabe Takashi, Matsuda Tomoki, Yamamoto Hironori, Tanaka Noriko, Lefor Alan Kawarai, Zhu Xin, Togashi Kazutomo
2023-Feb-02
artificial intelligence, colon cancer, colonoscopy, endoscopic treatability, white light image
