Machine Learning Predictive Models for Survival in Colorectal Cancer Patients With Diabetes: A Cohort Study of 10 749 Subjects

Abstract

OBJECTIVES: To identify the risk factors for the survival of colorectal cancer (CRC) patients with type 2 diabetes mellitus (T2DM), compare the predictive performance of models based on different algorithms, and develop a risk score system to predict the survival risk of the target population. METHODS: We analyzed data from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL), including 10 749 CRC patients with T2DM from 2000 to 2020. We employed traditional statistical methods and machine learning algorithms to compare their performance using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) analysis was conducted to identify risk factors and attribute model outputs. A risk score system was developed using the AutoScore-Survival package for risk stratification. RESULTS: Key predictors of CRC survival among T2DM patients included age at cancer diagnosis, sex, T2DM duration, alcohol consumption, central obesity, hypertension, levels of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and serum potassium, and anti-lipid drug usage. Among the models assessed, the random survival forest showed the best performance. The risk score system was calibrated as follows: age at diagnosis, T2DM duration, LDL-C, glycated hemoglobin, creatinine, and body mass index. The AUCs for 1, 3, and 5 years of the tuned risk score system were 0.746, 0.718, and 0.677, respectively. CONCLUSIONS: The random survival forest model provides superior survival prediction compared to other models evaluated. A validated risk score system has been established, facilitating risk stratification for clinicians to manage these patients.

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