Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

Mohan Timilsina, Samuele Buosi, Adrianna Janik, Pasquale Minervini, Luca Costabello, Maria Torrente, Mariano Provencio, Virginia Calvo, Carlos Camps, Ana L. Ortega, Bartomeu Massutí, M. Rosario Garcia Campelo, Edel del Barco, Joaquim Bosch-Barrera, Vít Novácek

Abstract: Lung cancer is one of the leading health complications causing high mortality worldwide. The relapsing behavior of medically treated early-stage lung cancer makes this disease even more complicated. Thus predicting such relapse using a data-centric approach provides a complementary perspective for clinicians to understand the disease. In this preliminary work, we explored off-the-shelf survival models to predict the relapse of early-stage lung cancer patients. We analyzed the survival models on a cohort of 1348 early-stage non-small cell lung cancer (NSCLC) patients in different timestamps. Using the prediction explanation model SHAP (SHapley Additive exPlanations), we further explained the best-performing survival model's predictions. Our explainable predictive model is a potential tool for oncologists that address an unmet clinical need for post-treatment patient stratification based on the relapse hazard.