Evidence map›Paper›PMID 42539910›Full record

ArticleJournal of the anus, rectum and colon2026

Development and Validation of a Computer-Aided Diagnostic System for Colorectal Lesions Based on the JNET Classification: Supporting the "Resect and Discard" Strategy.

Shin Morimoto, Shigeto Yoshida, Yongfei Wu, Tetsushi Koide, Toru Tamaki, Yudai Takehara, Yuki Kamigaichi, Hidenori Tanaka, Ken Yamashita, Yoshihiro Kishida and 2 more

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Article in Journal of the anus, rectum and colon, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Shin MorimotoDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Shigeto YoshidaDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Yongfei WuResearch Institute for Semiconductor Engineering, Hiroshima University, Hiroshima, Japan.
Tetsushi KoideResearch Institute for Semiconductor Engineering, Hiroshima University, Hiroshima, Japan.
Toru TamakiDepartment of Computer Science, Nagoya Institute of Technology, Nagoya, Japan.
Yudai TakeharaDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Yuki KamigaichiDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Hidenori TanakaDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Ken YamashitaDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Yoshihiro KishidaDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Toshio KuwaiGastrointestinal Endoscopy and Medicine, Hiroshima University Hospital, Hiroshima, Japan.
Shiro OkaDepartment of Gastroenterology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencecolonoscopycolorectal neoplasmscomputer-aided diagnosisnarrow band imaging

Identifiers

PMID42539910
PMCPMC13424650

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.