Age-group transportability of explainable machine-learning models for cancer-specific survival in early-onset colorectal cancer: a temporally validated U.S. SEER study

Preprint — not peer reviewed. This study evaluates the transportability and recalibration of colorectal cancer survival models across age groups using U.S. SEER registry data. Abstract Background: Early-onset colorectal cancer (EO-CRC, diagnosis at ages 18–49) is rising in incidence, yet prognostic models are predominantly developed in average-onset (AO) populations. Whether AO-trained survival models transport to EO patients without refitting — and whether simple recalibration repairs any degradation — has not been tested under a preregistered, temporally validated design.

Methods

Population-based cohort study using SEER 17-registry data (first malignant primary colorectal adenocarcinoma, diagnosed 2010–2019 at ages 18–84; N = 216,975). Models were trained on 2010–2016 diagnoses and tested temporally on 2017–2019 diagnoses (EO: 20,781 train / 9,551 test; AO: 130,728 / 55,915). The primary outcome was cancer-specific survival; the primary horizon (60 months) was locked by a prespecified follow-up-adequacy gate before any model was fitted. AO-trained Cox, XGBoost-Cox, and fixed-horizon XGBoost models were applied frozen — preprocessing included — to EO test patients. Prespecified materiality criteria defined transport degradation: paired ΔC-index ≥ 0.02 with 95% CI excluding 0; calibration slope outside 0.80–1.20; or |calibration-in-the-large (CITL)| > 0.20. Four recalibration variants, fitted on EO training data only, were evaluated against a prespecified adequacy range (slope 0.90–1.10 and |CITL| ≤ 0.10). SHAP analyses were descriptive only.