Outcome prediction algorithms for lung cancer will help lung cancer patients and professionals in shared decision making
euroCAT aimed to greatly improve cancer treatment and research by using a new internationally advanced computer network for clinical research and decision supporting software.
The goal of the project was twofold: a) to develop a shared database of medical characteristics in cancer patients, tumours and treatments. Copying data from existing databases and linking them together on a larger scale to improve the ability to learn and predict the outcome of individual treatments. b) find patients for trials and decrease and speed up the administration and analysis around clinical trials.
The euroCAT consortium and project ran from 2010 to 2015. It is in many aspects the seed project of the Personal Health Train as it was the first project (2010) to embark on distributed learning FAIR-avant-la-mot data.
Distributed learning infrastructure and new prediction models for survival and toxicities of treatments in lung cancer patients.
euroCAT showed that distributed learning across centers, borders and languages is possible.
euroCAT ultimately led to the Varian Learning Portal a professional distributed learning infrastructure in oncology made by Varian Medical Systems which is still in use.
André Dekker, Philippe Lambin
|Funders||The project was made possible through extensive, cross-border cooperation among the parties involved in addition to a sizeable European Interreg grant from Interreg IV-a.Interreg|
Nine organisations collaborated on the ambitious Euregional Computer Assisted Theragnostics project (EuroCAT) in the Meuse-Rhine region (The Netherlands, Belgium and Germany).
MAASTRO Clinic, Catharina Ziekenhuis Eindhoven, CHU Liege, Uniklinik RWTH Aachen, Limburg Oncologisch Centrum Hasselt, Siemens, Varian Medical Systems