Diagnostic Concordance Using Japan Narrow‐band Imaging Expert Team Classification for Diagnosing Colorectal Neoplasms: A Web‐based Diagnostic Concordance Study
Taku Sakamoto, Yasuhiko Mizuguchi, Hideki Ishikawa, Yoshitaka Murakami, Yutaka Saito

TL;DR
This study evaluates how consistently experts use the JNET classification to diagnose colorectal neoplasms through a web-based test.
Contribution
The study provides the first evaluation of diagnostic concordance among JNET core members using a web-based image test.
Findings
High agreement was found for SSL/HP lesions, but lower for neoplastic lesions like T1b cancer.
T1b cancer classification showed notable variability among experts.
Secondary findings influenced classification but were not the main focus of the study.
Abstract
The Japan Narrow‐band Imaging Expert Team (JNET) classification is widely used for magnified endoscopic diagnosis of colorectal neoplasms. However, its diagnostic concordance, particularly among the core members who contributed to its development, has not been sufficiently evaluated. Therefore, this study aimed to assess the diagnostic concordance of the JNET classification among JNET core members using a web‐based image interpretation test. A total of 27 JNET core members performed a web‐based static image reading test in two separate sessions. Each image was classified according to the JNET criteria, and the diagnostic concordance rate (DCR) was analyzed. Cases were categorized as having high (≥80% consensus), moderate (70%–79% consensus), or low (<70% consensus) agreement. The impact of secondary findings on diagnostic classification was explored for the secondary analysis.…
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Taxonomy
TopicsAI in cancer detection · Colorectal Cancer Screening and Detection · Radiomics and Machine Learning in Medical Imaging
