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Articles Published Processes
9/9/2026 9:45:12 AM | Browse: 2 | Download: 0
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Received |
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2026-04-08 01:00 |
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Peer-Review Started |
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2026-04-08 01:02 |
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First Decision by Editorial Office Director |
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2026-04-22 10:48 |
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Return for Revision |
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2026-04-22 11:19 |
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Revised |
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2026-05-06 07:36 |
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Publication Fee Transferred |
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2026-05-07 13:29 |
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Second Decision by Editor |
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2026-06-01 02:37 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-06-02 07:36 |
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Articles in Press |
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2026-06-02 07:36 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-08-21 09:25 |
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Publish the Manuscript Online |
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2026-09-09 09:45 |
| ISSN |
1948-5204 (online) |
| Open Access |
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. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yunus Halil Polat and Mehmet Kayaalp |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Mehmet Kayaalp, MD, Department of Medical Oncology, Ankara University, Tıp Fakültesi Street, Mamak 06620, Ankara, Türkiye. kayaalpmehmet2728@gmail.com |
| Key Words |
Colorectal cancer; Pre-colonoscopy triage; Machine learning; Gastroenterology; Precision medicine; Oncology; Decision support; Inflammatory indices |
| 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. |
| Publish Date |
2026-09-09 09:45 |
| Citation |
Polat YH, Kayaalp M. Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk. World J Gastrointest Oncol 2026; 18(9): 121970
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| URL |
https://www.wjgnet.com/1948-5204/full/v18/i9/121970.htm |
| DOI |
https://doi.org/10.4251/wjgo.121970 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
