prediction model

Data from: Developing and validating a survival prediction model for NSCLC patients through distributed learning across three countries

Jochems A, Deist TM, Naqa IEl, et al. Data from: Developing and validating a survival prediction model for NSCLC patients through distributed learning across three countries. 2017. doi:10.17195/candat.2017.02.2.

Developing and validating a survival prediction model for NSCLC patients through distributed learning across three countries

Arthur Jochems, Timo M. Deist, Issam El Naqa, Marc Kessler, Chuck Mayo, Jackson Reeves, Shruti Jolly, Martha Matuszak, Randall Ten Haken, Johan van Soest, Cary Oberije, Corinne Faivre-Finn, Gareth Price, Dirk de Ruysscher, Philippe Lambin, Andre Dekker

 

Purpose

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

A validated prediction model for overall survival from Stage III Non Small Cell Lung Cancer: towards survival prediction for individual patients.

Cary Oberije, Dirk De Ruysscher, Ruud Houben, Michel van de Heuvel, Wilma Uyterlinde, Joseph Deasy, Jose Belderbos, Anne-Marie C. Dingemans, Andreas Rimner, Shaun Din, Philippe Lambin

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.