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

Figure 1 Receiver operating characteristic curves for malignancy prediction. Receiver operating characteristic curves based on out-of-fold predicted probabilities from repeated stratified 5-fold cross-validation (50 repeats) comparing the full-feature model, SHAP10, and SHAP5 models. Area under the curve values are reported in the text/Table 2. ROC: Receiver operating characteristic curve; CV: Cross-validation; OOF: Out-of-fold; AUC: Area under the curve; SHAP: SHapley Additive exPlanations; FPR: False-positive rate.

Figure 2 Precision-recall curves based on out-of-fold predicted probabilities from repeated stratified cross-validation. The dashed horizontal line indicates the baseline precision equal to the outcome prevalence (1.43%). Area under the precision-recall curve values are reported in the text/Table 2. PR: Precision-recall; SHAP: SHapley Additive exPlanations.

Figure 3 Beeswarm plot showing the distribution of SHapley Additive exPlanations values for the top-10 predictors (English labels). Each dot represents an individual patient; X-axis indicates SHapley Additive exPlanations value (impact on model output), and color denotes the feature value (low to high). SHAP: SHapley Additive exPlanations; NLR: Neutrophil to lymphocyte ratio; HDL: High-density lipoprotein; SII: Systemic immune-inflammation index; NHR: Neutrophil-to-high-density lipoprotein ratio; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase.

Figure 4 Decision curve analysis. Decision curve analysis based on out-of-fold predicted probabilities from repeated stratified cross-validation. A: Standard net benefit curves across threshold probabilities, comparing the full-feature, SHAP10, and SHAP5 models with “refer all” and “refer none” strategies; B: Rule-out-oriented clinical yield expressed as net avoided colonoscopies per 100 patients, calculated as true negatives gained minus harm-weighted false negatives. OOF: Out-of-fold; SHAP: SHapley Additive exPlanations; TN: True negative; FN: False negative.