Arrow Left Health research

Use case: The Dutch Network of Computer Assisted Theragnostics (duCAT)

Develop and validate a framework for clinical cancer research, incorporating predictive models developed by machine learning based on data from multiple institutions.

Medical relevance

New models that predict treatment outcomes for lung cancer patients will help physicians and patients to make better decisions

Description

The objective of this project was to develop a new framework for clinical cancer research. This revolutionary IT solution more explicitly takes account of the properties of an individual patient in the development and translation to clinical trials on new diagnostic and therapeutic modalities.

In this project we used machine learning to develop new prediction models with the aim to predict survival, radiation induced lung damage and esophagitis. The high accuracy of these prediction models is allows it to be used in clinical practice in the participating centres. duCAT ran from 2011 to 2018 and used distributed learning as its core approach. The tools, knowledge and technologies it generated formed the basis of the PHT. The Varian Learning Portal provides a commercial solution.

DuCAT was built upon the CAT database system implemented at MAASTRO and developed by Siemens. The vision for this system is one that can be used by clinical researchers and pharmaceutical companies to advance clinical research in the Netherlands.

Main results

Further development and deployment of a distributed learning infrastructure (Varian Learning Portal) in the Netherlands.

Lessons learned

It is possible to learn and validate meaningful outcome prediction models from Dutch routine cancer care data

Follow up

The duCAT partners participated in the 20k challenge and many also in the PROTRAIT project

Project details

Project leader

André Dekker, Philippe Lambin

Funders STW (duCAT)
Collaboration partners

NKI Amsterdam / Radboud Nijmegen / MAASTRO Clinic / Erasmus MC / Siemens / Varian

 

Contact person

References

Decision support systems for personalized and participative radiation oncology
Advanced Drug Delivery Reviews. Vol 109, 15 January 2017, p. 131-153
https://doi.org/10.1016/j.addr.2016.01.006

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