Predicting survival in small cell lung cancer patients undergoing various treatments: a machine learning approach.

Abstract

Background: Small cell lung cancer (SCLC) is highly metastatic, accounting for 1.796 million global cancer-related deaths in 2020, with no established standard care. This study aimed to assess treatment effects on SCLC patient survival across stages and develop a machine learning-based survival prediction tool for accurate overall survival (OS) estimation.

Methods: We developed four prediction models: Cox proportional hazard (Cox PH) regression, survival tree (ST), random survival forest (RSF), and gradient boosting survival analysis (GBSA). Patients were randomly split 7:3 into training and test datasets, with 10-fold cross-validation and 50 iterations on the training dataset. Cox PH used hazard ratios, while the other models employed importance values to assess feature predictiveness. Harrell's C-index (C-index) and Brier score (BS) measured model performance, with internal validations using R version 4.2.0.

Results: Cox PH outperformed others based on mean C-index and BS. Multivariate analysis across models highlighted distant metastases (M category), tumor stage, and treatment modalities (radiotherapy, chemotherapy, surgery) as key survival predictors. Stratified Cox PH analysis revealed surgery's efficacy in early-stage SCLC (stage II) and radiotherapy's advantage in stage III. Homogeneity was observed in chemotherapy benefits across cancer stages.

Conclusions: Surgery, chemotherapy, and radiotherapy are integral in SCLC treatment, contingent on cancer stage and characteristics. Surgery offers promise for early-stage cases, while advanced-stage strategies require further exploration.