Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk

doi:10.4251/wjgo.121970

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Oncol. Sep 15, 2026; 18(9): 121970
Published online Sep 15, 2026. doi: 10.4251/wjgo.121970

Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk

Yunus Halil Polat, Mehmet Kayaalp

Yunus Halil Polat, Department of Gastroenterology, Ankara Training and Research Hospital, Ankara 06370, Ankara, Türkiye

Mehmet Kayaalp, Department of Medical Oncology, Ankara University, Mamak 06620, Ankara, Türkiye

Author contributions: Polat YH contributed to data curation, investigation, supervision, project administration, and resources; Kayaalp M contributed to conceptualization, methodology, software, formal analysis, validation, and visualization; Polat YH and Kayaalp M contributed to writing - original draft.

AI contribution statement: AI tools (specifically ChatGPT) were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.

Institutional review board statement: This study was approved by the Institutional Review Board of Ankara Training and Research Hospital (Approval No. E-25/629). We confirm that all procedures were conducted in accordance with ethical standards and the Declaration of Helsinki.

Informed consent statement: This study is a retrospective observational study based on previously recorded clinical data. No direct patient contact or intervention was performed. According to the Institutional Review Board approval, the requirement for obtaining informed consent was waived due to the retrospective nature of the study.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.

Data sharing statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Corresponding author: Mehmet Kayaalp, MD, Department of Medical Oncology, Ankara University, Tıp Fakültesi Street, Mamak 06620, Ankara, Türkiye. kayaalpmehmet2728@gmail.com

Received: April 8, 2026
Revised: May 6, 2026
Accepted: June 2, 2026
Published online: September 15, 2026
Processing time: 142 Days and 1.2 Hours

Core Tip

Core Tip: Colonoscopy capacity is limited and most procedures find no malignancy. Using only routinely available pre-colonoscopy laboratory parameters from 1604 consecutive patients (1.43% malignancy prevalence), we developed an extreme gradient boosting-based machine learning model with explainable SHapley Additive exPlanations analysis. Performance was validated through repeated stratified cross-validation, calibration analysis, and decision-curve analysis. The 10-feature SHapley Additive exPlanations-reduced model achieved a high sensitivity of 91% and a very low negative likelihood ratio of 0.17, with a calibrated negative predictive value of 99.7%. This rule-out-oriented tool may safely defer 40 to 67 colonoscopies per 100 patients in resource-limited settings.