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...
| Publicado en: | International Journal of Radiation Oncology, Biology, Physics Vol. 99; no. 2; pp. 344 - 353 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Pergamon Press - An Imprint of Elsevier Science
Oct2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124740438&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124740438 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03603016 1ZQ jtl: International Journal of Radiation Oncology, Biology, Physics issn: 03603016 maglogo: N pubinfo: dt: Oct2017 vid: 99 iid: 2 pid: 2410 pub: Pergamon Press - An Imprint of Elsevier Science artinfo: ui: 124740438 124740438 NLM28871984 124740438 10.1016/j.ijrobp.2017.04.021 NLM28871984 124740438 ppf: 344 ppct: 9 formats: tig: atl: Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. aug: au: 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 affil: Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands sug: subj: Carcinoma, Non-Small-Cell Lung Mortality Learning Lung Neoplasms Therapy Carcinoma, Non-Small-Cell Lung Therapy Lung Neoplasms Mortality Prospective Studies Models, Statistical Pharmacokinetics Aged Female Kaplan-Meier Estimator Severity of Illness Indices Resource Databases Neoplasm Staging Standards Lymph Nodes Pathology Radiotherapy, Conformal Mortality Antineoplastic Agents, Combined Therapeutic Use Time Factors Human Male Forecasting Probability Age Factors Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source Aged: 65+ years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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