DESIREE is a European Union’s Horizon 2020 project from the Research and Innovation Programme under grant agreement No 690238 running from 1st February 2016.
The DESIREE project aims to provide a web-based software ecosystem for the personalized, collaborative and multidisciplinary management of Primary Breast Cancer by specialized Breast Units (BU), from diagnosis to therapy and follow-up. For that DESIREE provides decision support on the available therapy options by incorporating experience from previous cases and outcomes, and thus, going beyond the limitations of existing guideline-based decision support systems (DSS).
Specific interactive tools will be also developed for quantitatively analysing medical images and fusing (registering) different imaging modalities with complementary information for enhanced insight. The system will be also connected to a genomic and bioinformatics platform leveraging NGS data offered by Sistemas Genomicos (SIG) that will help personalized diagnostic and treatment and provide valuable information about predictive value in the outcomes of some treatments, based on the patient individual characteristics.
Another objective of DESIREE is to develop a virtual surgery tool that could be applied in clinical practice, based on a multi-scale physiological model that predicts the outcome of breast conservative therapy (BCT). The model will have important physical and psychological implications for the patient regarding the outcome of conservative surgery and will enhance the patient-physician interaction when choosing the treatment.
Finally, the heterogeneous data and knowledge bases, the DSS and the analysis and modelling tools will be integrated into a secure web-based software infrastructure. This web-based software will be provided with highly visual interfaces for exploring the patient case. It will bring the necessary features for enhancing the coordination and management of different patient cases in the BUs and for exploring all the accumulated heterogeneous multi-scale information from previous patient cases in an intuitive manner.