Development and Validation of a Computer-Aided Diagnostic System for Colorectal Lesions Based on the JNET Classification: Supporting the "Resect and Discard" Strategy
Year: 2026
Type: article
Abstract: Objectives: Although the Japan NBI Expert Team (JNET) classification for colorectal lesions is clinically useful, standardization remains challenging owing to endoscopist variabilities. Therefore, we developed a computer-aided diagnosis (CADx) system to support JNET diagnoses, potentially facilitating a "Resect and Discard" strategy. The CADx system's diagnostic performance and its potential to support the "Resect and Discard" strategy were compared with those of endoscopists of varying experience. Methods: Forty-three patients (60 lesions) who underwent CADx system colonoscopies at Hiroshima University Hospital between September 2022 and July 2024 were evaluated. Endoscopists were grouped by experience: beginners (100-500 cases, n=4), intermediates (1,000-2,000 cases, n=5), and experts (>2,000 cases, n=3). In Study 1, JNET classification concordance between the CADx system and beginner/intermediate endoscopists was compared, using expert diagnosis as the gold standard. Study 2 included 38 <10 mm lesions, with histopathology as the gold standard. Lesions excluding advanced neoplasia were classified as "Resect and Discard". The CADx system and endoscopists were evaluated for identifying JNET Type 1 and 2A lesions as non-advanced neoplasia. Results: In Study 1, CADx-expert concordance was 83.3% (Kappa=0.75)-significantly higher than beginners (66.7%) and outperforming intermediates (78.0%). In Study 2, the CADx system's accuracy was 92.1%-significantly higher than beginners (70.4%) and outperforming intermediates (82.6%) and experts (78.9%). The CADx system's advanced neoplasia negative predictive value was 100%. Conclusions: The CADx system showed high concordance with expert diagnoses. For lesions <10 mm, its high advanced neoplasia diagnostic accuracy indicates its potential utility in supporting the "Resect and Discard" strategy.
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,507
OpenAlex ID: W7170826137
