Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy have limited quality. Better survival prediction will help choosing treatment for lung cancer patients
In an international consortium consisting of cancer centers in the USA, UK and Netherlands a distributed learning infrastructure was deployed. A novel prediction model based on Bayesian Networks was developed by machine learning and validated based on the data of these cancer centers. The project was conducted in 2016 and was the first time distributed learning was used with UK and USA cancer centers, by means of a commercial PHT implementation (from Varian Medical System “Varian Learning Portal”),.
A new Bayesian Network based prediction model for survival in lung cancer outperforming previous models.
Bayesian networks can be learned in distributed manner. A distributed learning infrastructure is also acceptable and deployable in the USA
Ann Arbor Michigan continues to drive this project and is focusing now on developing an ontology to support future projects
|Funders||Dutch Technology Foundation STW (duCAT)|
|Collaboration partners||MAASTRO Clinic / University of Michigan, Ann Arbor, Michigan / The Christie NHS Foundation Trust, Manchester, UK / Varian|