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

Abstract

BACKGROUND

Colonoscopy is the gold standard for colorectal cancer diagnosis but is invasive and subject to capacity constraints. Noninvasive precolonoscopy triage tools are needed to prioritize high-risk patients and safely manage those with a low likelihood of malignancy.

AIM

To develop an extreme gradient boosting (XGBoost)-based prediction model using routine laboratory parameters to estimate precolonoscopy malignancy risk.

METHODS

This retrospective cohort study included 1604 consecutive patients who underwent colonoscopy at Ankara Training and Research Hospital (January 2022 to December 2025). The predictors included age, sex, complete blood count components, liver enzymes, lipid profiles, fasting glucose levels, and derived inflammatory/metabolic indices (neutrophil to lymphocyte ratio, systemic immune-inflammation index, neutrophil-to-high-density lipoprotein ratio, platelet to lymphocyte ratio, triglyceride-glucose, atherogenic index of plasma, and Fibrosis-4). An XGBoost classifier with class-weight adjustment was evaluated using repeated stratified K-fold cross-validation (5 folds, 50 repeats). SHapley Additive exPlanations (SHAP) analysis guided feature reduction to ten-feature (SHAP10) and five-feature (SHAP5) models.

RESULTS

Malignancy was present in 23 patients (1.43%). The full model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.734 and an area under the precision-recall curve of 0.078; at the Youden-optimized threshold, the positive predictive value was 0.050, and the negative predictive value (NPV) was 0.993. The discrimination ability of the SHAP10 model was comparable (ROC-AUC: 0.736; NPV: 0.997), whereas that of the SHAP5 model was lower (ROC-AUC: 0.711). The key predictors included glucose level, platelet count, neutrophil to lymphocyte ratio, age, neutrophil count, alanine aminotransferase level, aspartate aminotransferase level, systemic immune-inflammation index, neutrophil-to-high-density lipoprotein ratio, and high-density lipoprotein level. After post hoc Platt recalibration, all three models achieved near-ideal calibration (intercept ≈ 0, slope ≈ 1.00).

CONCLUSION

An XGBoost model using routine precolonoscopy laboratory data achieved a very high NPV, supporting its potential as a rule-out-oriented triage tool. A parsimonious ten-feature model preserved discrimination while enhancing clinical applicability. Prospective, multicenter validation is warranted.

Keywords: 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.