Development and external validation of a machine-learning risk model for colorectal cancer triage in Sweden's fast-track pathway.

BACKGROUND: Fast-track colonoscopy pathways detect colorectal cancer (CRC) but strain endoscopy capacity. We developed and externally validated multivariable risk models combining faecal immunochemical test (FIT) with clinical data to triage Swedish fast-track referrals. METHODS: We analysed 2,539 fast-track colonoscopies (2016-2020) and 723 as a validation cohort (2021-2022). Predictors were age, sex, eight symptoms, binary FIT, haemoglobin, and suspicious imaging. In development, missing predictors were imputed within training/test splits; validation used complete cases. Class imbalance was handled with oversampling to balance classes. Logistic regression and CatBoost models were built. External validation assessed AUC, calibration, and calibration slope. Variable influence was examined with feature contribution analysis. Decision-curve analysis (DCA) used the entry rule as a treat-all baseline. RESULTS: CRC prevalence was 16% in both cohorts. Development AUCs were 0.81 (95% CI 0.78-0.83) for CatBoost and 0.77 (0.74-0.80) for logistic regression. In external validation, AUCs were 0.67 (0.61-0.74) and 0.66 (0.58-0.75), respectively. FIT status, suspicious imaging, and age were the main predictors. Calibration drift was observed. The entry rule functions as treat-all (specificity 0%). CatBoost DCA curves were near or above treat-all at common thresholds, but 95% bands overlapped; advantage over treat-all was not confirmed. CONCLUSIONS: A multivariable approach using FIT, simple laboratory data, and clinical findings can support triage in a fast-track pathway. In validation, discrimination was modest, and superiority over the entry rule could not be shown because all referred patients underwent colonoscopy. External testing across regions, recording quantitative FIT, and routine recalibration are needed before implementation.