@article {79, title = {Data from: Developing and validating a survival prediction model for NSCLC patients through distributed learning across three countries}, year = {2017}, publisher = {CancerData}, abstract = {

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

Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected and stored in 2 different cancer institutes (559 patients at Maastro clinic (Netherlands), 139 at University of Manchester (UK). The model was further validated on 196 patients originating from the University of Michigan (USA).

A Bayesian network model is adapted for distributed learning (watch the animation). Two-year post-treatment survival was chosen as endpoint.\ The Institute 1 cohort data is publicly available\ and the developed models can be found at\ PredictCancer.org)

Results

Variables included in the final model were T and N stage, age, performance status, and total tumor dose. The model has an 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 T and N stage performed with an AUC of 0.47 on the validation set, significantly worse than our model (P\<0.001). A high- and low-risk chance of survival group can be identified using the model presented in this study, these groups have significantly different overall survival (P\<0.01).

Conclusion

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.

}, keywords = {Bayesian network, lung cancer, NSCLC, prediction model}, doi = {10.17195/candat.2017.02.2}, author = {Arthur Jochems and Timo M. Deist and Issam El Naqa and Marc Kessler and Chuck Mayo and Jackson Reeves and Shruti Jolly and Martha Matuszak and Randall Ten Haken and Johan van Soest and Cary Oberije and Corinne Faivre-Finn and Gareth Price and Dirk De Ruysscher and Philippe Lambin and Andr{\'e} Dekker} } @article {57, title = {Data from: Development and Evaluation of an Online Three-Level Proton vs Photon Decision Support Prototype for Head and Neck Cancer - Comparison of Dose, Toxicity and Cost-Effectiveness}, year = {2015}, month = {10/2015}, publisher = {CancerData}, abstract = {

This dataset supports the publication by Cheng et al, entitled "Development and Evaluation of an Online Three-Level Proton vs Photon Decision Support Prototype for Head and Neck Cancer - Comparison of Dose, Toxicity and Cost-Effectiveness". It\ was used to evaluate the PRODECIS decision support prototype\ for online evaluation of eligiblilty for proton therapy using Dutch guidelines.\ 

The Head and Neck test dataset was based on the ROCOCO dataset by HP van der Laan, as described in doi:10.3109/0284186X.2012.692885.

}, keywords = {cancer, decision support, model-based, oncology, proton therapy, radiotherapy planning}, doi = {10.17195/candat.2015.10.5}, url = {https://www.cancerdata.org/resource/doi:10.17195/candat.2015.10.5}, author = {Qing Cheng and Erik Roelofs and Bram Ramaekers and Danielle Eekers and Johan van Soest and Tim Lustberg and Frank Hoebers and Hans Paul van der Laan and Erik Korevaar and Andr{\'e} Dekker and Johannes Langendijk and Philippe Lambin} } @article {33, title = {Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial}, journal = {Radiotherapy and Oncology}, volume = {108}, year = {2013}, month = {Jan-07-2013}, pages = {174 - 179}, issn = {01678140}, doi = {10.1016/j.radonc.2012.09.019}, author = {Erik Roelofs and Persoon, Lucas and Nijsten, Sebastiaan and Wiessler, Wolfgang and Andr{\'e} Dekker and P. Lambin} } @article {34, title = {Cardiac comorbidity is an independent risk factor for radiation-induced lung toxicity in lung cancer patients}, journal = {Radiotherapy and Oncology}, volume = {109}, year = {2013}, month = {Jan-10-2013}, pages = {100 - 106}, issn = {01678140}, doi = {10.1016/j.radonc.2013.08.035}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0167814013004350}, author = {Nalbantov, Georgi and Kietselaer, Bas and Vandecasteele, Katrien and Cary Oberije and Maaike Berbee and Esther G C Troost and Anne-Marie C. Dingemans and Baardwijk, Angela van and Smits, Kim and Andr{\'e} Dekker and Johan Bussink and Dirk De Ruysscher and Lievens, Yolande and P. Lambin} } Error | CancerData.org

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