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Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries
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 se...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Science Inc
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575360/ https://www.ncbi.nlm.nih.gov/pubmed/28871984 http://dx.doi.org/10.1016/j.ijrobp.2017.04.021 |
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author | Jochems, Arthur Deist, Timo M. El Naqa, Issam Kessler, Marc Mayo, Chuck Reeves, Jackson Jolly, Shruti Matuszak, Martha Ten Haken, Randall van Soest, Johan Oberije, Cary Faivre-Finn, Corinne Price, Gareth de Ruysscher, Dirk Lambin, Philippe Dekker, Andre |
author_facet | Jochems, Arthur Deist, Timo M. El Naqa, Issam Kessler, Marc Mayo, Chuck Reeves, Jackson Jolly, Shruti Matuszak, Martha Ten Haken, Randall van Soest, Johan Oberije, Cary Faivre-Finn, Corinne Price, Gareth de Ruysscher, Dirk Lambin, Philippe Dekker, Andre |
author_sort | Jochems, Arthur |
collection | PubMed |
description | 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<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). 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. |
format | Online Article Text |
id | pubmed-5575360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier Science Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-55753602017-10-01 Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries Jochems, Arthur Deist, Timo M. El Naqa, Issam Kessler, Marc Mayo, Chuck Reeves, Jackson Jolly, Shruti Matuszak, Martha Ten Haken, Randall van Soest, Johan Oberije, Cary Faivre-Finn, Corinne Price, Gareth de Ruysscher, Dirk Lambin, Philippe Dekker, Andre Int J Radiat Oncol Biol Phys Physics Contribution 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<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). 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. Elsevier Science Inc 2017-10-01 /pmc/articles/PMC5575360/ /pubmed/28871984 http://dx.doi.org/10.1016/j.ijrobp.2017.04.021 Text en © 2017 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Physics Contribution Jochems, Arthur Deist, Timo M. El Naqa, Issam Kessler, Marc Mayo, Chuck Reeves, Jackson Jolly, Shruti Matuszak, Martha Ten Haken, Randall van Soest, Johan Oberije, Cary Faivre-Finn, Corinne Price, Gareth de Ruysscher, Dirk Lambin, Philippe Dekker, Andre Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries |
title | Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries |
title_full | Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries |
title_fullStr | Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries |
title_full_unstemmed | Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries |
title_short | Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries |
title_sort | developing and validating a survival prediction model for nsclc patients through distributed learning across 3 countries |
topic | Physics Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575360/ https://www.ncbi.nlm.nih.gov/pubmed/28871984 http://dx.doi.org/10.1016/j.ijrobp.2017.04.021 |
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