Predicting three-year risk of incident colorectal precancerous conditions using non-invasive predictors and machine-learning algorithms: A multi-center retrospective study

doi:10.3748/wjg.121223

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 32, Issue 46

This Article

(4)

(5)

(0)

(61)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

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

Retrospective Study

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 Gastroenterol. Dec 14, 2026; 32(46): 121223
Published online Dec 14, 2026. doi: 10.3748/wjg.121223

Table 3 Development of a logistic model in the training cohort

Predictor	Odds ratio	95%CI	Coefficient	P value
Univariate regression
STK1p (pmol/L)	1.69	1.10-2.61	0.5262	0.017
CYFRA21-1 (μg/L)	1.31	1.11-1.56	0.2717	0.002
DBP (mmHg)	1.04	1.02-1.07	0.0407	0.001
Age (years)	1.04	1.01-1.08	0.0420	0.007
CA19-9 (kU/L)	1.03	1.003-1.05	0.0265	0.026
Female sex	1.10	0.57-2.10	0.0929	0.779
FIT positive	1.24	0.68-2.25	0.2119	0.489
Stepwise multivariate regression
STK1p (pmol/L)	1.84	1.15-2.94	0.6084	0.011
CYFRA21-1 (μg/L)	1.32	1.10-1.59	0.2794	0.003
DBP (mmHg)	1.04	1.01-1.07	0.0375	0.004
Constant	0.002	0.0002-0.01	-6.3842	< 0.001

CA19-9: Carbohydrate antigen 19-9; CYFRA21-1: Cytokeratin 19 fragment; DBP: Diastolic blood pressure; FIT: Fecal immunochemical test; STK1p: Serum thymidine kinase 1 protein.

Full Size Table

Citation: Huang XY, Zhao L, He H, Peng R, Xue H, Lai SM, Wu WX, Qiu XX, Cai XH, He JH, Qin WJ, Zhou WT, Wu WQ, Lin YP. Predicting three-year risk of incident colorectal precancerous conditions using non-invasive predictors and machine-learning algorithms: A multi-center retrospective study. World J Gastroenterol 2026; 32(46): 121223
URL: https://www.wjgnet.com/1007-9327/full/v32/i46/121223.htm
DOI: https://dx.doi.org/10.3748/wjg.121223

Huang XY, Zhao L, He H, Peng R, Xue H, Lai SM, Wu WX, Qiu XX, Cai XH, He JH, Qin WJ, Zhou WT, Wu WQ, Lin YP. Predicting three-year risk of incident colorectal precancerous conditions using non-invasive predictors and machine-learning algorithms: A multi-center retrospective study. World J Gastroenterol 2026; 32(46): 121223 [DOI: 10.3748/wjg.121223]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.