Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with (chemo)radiotherapy are of limited quality. In this work, we develop a predictive model of survival at two years based on a large volume of historical patient data, as a proof of concept, using a distributed learning approach.
Patients and methods
Purpose: Although homogeneous according to TNM staging system, stage III NSCLC patients form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but also complicate treatment decision making. Decision Support Systems (DSS), providing individualized prognostic information, can overcome this, but are currently lacking. A DSS for stage III NSCLC requires development and integration of multiple models.