Published online Dec 14, 2026. doi: 10.3748/wjg.121708
Revised: April 8, 2026
Accepted: May 26, 2026
Published online: December 14, 2026
Processing time: 203 Days and 19.2 Hours
Colorectal cancer (CRC) ranks third in global incidence and second in cancer-related mortality. The lung is the second most common distant metastatic site, and pulmonary metastasis substantially worsens long-term survival. Although tumor-associated inflammation and lipid metabolic reprogramming synergistically drive the metastatic cascade, systematic integration of these biomarkers into predictive models remains limited. Whether preoperative routine hematological indices alone can achieve preliminary risk stratification - particularly before pathological results become available or in resource-limited primary care settings - has not been systematically investigated.
To develop and validate an interpretable machine learning model integrating inflammation-lipid metabolism biomarkers for CRC pulmonary metastasis predi
A total of 1246 CRC patients from two centers were enrolled. The development cohort was split 7:3 into training (n = 621) and internal validation (n = 267) sets; an external cohort (n = 358) independently assessed generalizability. Sixteen candidate predictors spanning demographics (5), systemic inflammatory indices (8), and lipid metabolism indices (3) were incorporated via a four-layer progres
Progressive modeling demonstrated incrementally improved performance with sequential introduction of inflammatory and lipid metabolism features. LightGBM achieved the best individual performance [area under the curve (AUC) = 0.7507, 95% confidence interval: 0.6872-0.8157]. Ensemble methods further enhanced performance; Weighted Voting attained the highest internal validation AUC (0.7513) and maintained robust external generalization (AUC = 0.7112, Brier = 0.1850). SHAP global analysis identified lymphocyte-to-C-reactive protein ratio (LCR), body mass index (BMI), and apolipoprotein B-to-apolipoprotein A1 ratio as the top three predictors, indicating that inflammatory-immune balance, metabolic status, and lipoprotein dysregulation constitute core dimensions driving risk prediction. Dependence plots revealed key thresholds (LCR < 0.5, BMI > 25) informing potential clinical risk stratification.
This multimodal inflammation-lipid metabolism model effectively predicts CRC pulmonary metastasis risk, with cross-center transportability confirmed through external validation. SHAP analysis supports the model's clinical credibility and biological plausibility. All predictors are obtainable through routine clinical testing without specialized equipment, enabling deployment in primary care settings. The model has been implemented as an interactive web application for individualized risk assessment and visual decision support.
Core Tip: No prior model predicts colorectal cancer pulmonary metastasis using only preoperative routine blood-derived inflammation and lipid metabolism biomarkers. In this 1246-patient, dual-center study, we systematically compared 14 machine learning algorithms and three ensemble strategies - the most extensive benchmark reported for this task. The optimal Weighted Voting ensemble achieved validated discrimination [internal area under the curve (AUC) = 0.7513; external AUC = 0.7112] using 16 readily obtainable predictors. SHapley Additive exPlanations interpretability analysis revealed the lymphocyte-to-C-reactive protein ratio and body mass index as dominant predictors, offering a practical risk-stratification tool before pathological staging becomes available.