Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

57 Citations (Web of Science)

Abstract

Purpose: Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach.

Methods and Materials: Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [ United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org.

Results: Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P

Conclusions: Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care. (C) 2017 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Original languageEnglish
Pages (from-to)344-352
Number of pages9
JournalInternational Journal of Radiation Oncology Biology Physics
Volume99
Issue number2
DOIs
Publication statusPublished - 1 Oct 2017

Keywords

  • CELL-LUNG-CANCER
  • RECURSIVE PARTITIONING ANALYSIS
  • ONCOLOGY GROUP RTOG
  • GROSS TUMOR VOLUME
  • RADIATION-THERAPY
  • EXTERNAL VALIDATION
  • PROGNOSTIC-FACTORS
  • 2-YEAR SURVIVAL
  • DOSE-ESCALATION
  • HEALTH-CARE

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