Investigating AI Approaches for Survival Prediction in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting survival in CLL, comparing the performance of Random Survival Forest (RSF), Decision Tree (DT), and Cox proportional hazards models across two cohorts: MYC-positive patients and a general CLL population. Three time-to-event outcomes were assessed: 10-year from diagnosis, 10-year from cytogenetic assessment, and time to first treatment. Model performance was evaluated using the C-index and AUC, revealing that RSF and DT models outperformed Cox models in predictive accuracy. Permutation importance highlighted key predictive variables; however, RSF and DT models pose interpretability challenges compared to Cox models, which offer clear hazard ratios. Additionally, an interactive application is available via Streamlit, and the source code is open access on GitHub. Despite limitations in dataset size and external validity, ML models show promise for personalized survival predictions in CLL, especially for MYC-positive cases, underscoring the potential for further model refinement to enhance clinical usability.