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

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World Journal of Gastroenterology

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1007-9327

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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 1 Baseline characteristics and incident events of study participants, median (interquartile range)/n (%)

Characteristics	Training	Internal validation	External validation	P value	Overall
n	762	761	285		1808
Incident AA	53 (7.0)	52 (6.8)	26 (9.1)	0.410	131 (7.3)
Site of incident AA				0.012
Rectum	6 (0.8)	19 (2.5)	10 (3.5)		35 (1.9)
Left colon	28 (3.7)	14 (1.8)	6 (2.1)		48 (2.7)
Right colon	19 (2.5)	19 (2.5)	10 (3.5)		48 (2.7)
Age (years)	48 (41, 54)	47 (40, 53)	51 (43, 59)	< 0.001	48 (41, 54)
Female sex	175 (23.0)	164 (21.6)	106 (37.2)	< 0.001	445 (24.6)
DBP (mmHg)	74 (66, 82)	73 (66, 82)	76 (69, 84)	0.020	74 (66, 82)
STK1p (pmol/L)	0.16 (0.10, 0.24)	0.16 (0.10, 0.23)	0.33 (0.17, 0.72)	< 0.001	0.17 (0.11, 0.26)
FIT positive	213 (27.95)	216 (28.38)	77 (27.0)	0.908	506 (28.0)
CYFRA21-1 (μg/L)	2.00 (1.42, 2.70)	1.92 (1.44, 2.80)	1.26 (0.95, 1.42)	< 0.001	1.83 (1.26, 2.60)
CA19-9 (kU/L)	6.90 (3.62, 11.08)	6.72 (3.75, 11.27)	6.69 (3.50, 11.53)	0.959	6.77 (3.62, 11.24)

Advanced adenoma (including adenoma ≥ 10 mm, with villous components, or exhibiting high-grade dysplasia) diagnosis based on the National Comprehensive Cancer Network guideline. AA: Advanced adenoma; 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.

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Table 2 Predictors selected by the least absolute shrinkage and selection operator procedure

Lambda	Number of non-zero coefficient	Cross-validation mean deviance	Variables (A)dded, (R)emoved, or left (U)nchanged lambda
0.0513	1	0.5032	A	STK1p
0.0388	2	0.4959	A	CA19-9
0.0294	3	0.4938	A	CYFRA21-1
0.0268	4	0.4936	A	DBP
0.0153	5	0.4805	A	Age
0.0088	6	0.4764	A	FIT positive
0.0073¹	7	0.4762	A	Sex

¹Lambda selected by the minimum cross-validation mean deviance.

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.

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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.

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Table 4 Performance of different models for predicting advanced adenoma

Model	Area under the curve (95%CI)	Threshold	Sensitivity	Specificity
Training
XGBOOST (probability)	0.988 (0.980-0.996)	≥ 50%	96.23%	99.86%
LASSO (probability)	0.682 (0.596-0.768)	≥ 7.43%	50.94%	84.77%
Decision tree (1 vs 0)	0.667 (0.602-0.733)	≥ 1	35.85%	97.60%
NNET (1 vs 0)	0.575 (0.527-0.624)	≥ 1	9.43%	99.58%
SVM (1 vs 0)	0.519 (0.493-0.545)	≥ 1	3.77%	100.00%
Logistic model (probability)	0.691 (0.607-0.774)	≥ 8.87%	50.94%	85.47%
Internal validation
XGBOOST (probability)	0.825 (0.766-0.883)	≥ 50%	63.46%	99.86%
LASSO (probability)	0.616 (0.538-0.693)	≥ 7.43%	26.92%	83.36%
Decision tree (1 vs 0)	0.546 (0.502-0.590)	≥ 1	11.54%	97.60%
NNET (1 vs 0)	0.492 (0.488-0.497)	≥ 1	0.00%	98.45%
SVM (1 vs 0)	0.499 (0.498-0.501)	≥ 1	0.00%	99.86%
Logistic model (probability)	0.622 (0.545-0.698)	≥ 8.87%	26.92%	84.06%
External validation
XGBOOST (probability)	0.767 (0.642-0.892)	≥ 50%	61.54%	90.35%
LASSO (probability)	0.500 (0.500-0.500)	≥ 7.43%	-	-
Decision tree (1 vs 0)	0.561 (0.477-0.646)	≥ 1	23.08%	89.19%
NNET (1 vs 0)	0.552 (0.489-0.615)	≥ 1	11.54%	98.84%
SVM (1 vs 0)	0.500 (0.500-0.500)	≥ 1	-	-
Logistic model (probability)	0.706 (0.602-0.809)	≥ 8.87%	69.23%	67.95%

LASSO: Least absolute shrinkage and selection operator; NNET: Neural network; SVM: Support vector machine; XGBOOST: EXtreme Gradient Boosting.

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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]

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