General objectives

The general objectives pursued by the development of the DESIREE project can be summarized as follow

  • Improve the coordination and multidisciplinary management of breast cancer cases in Breast Units, allowing handling a vast amount of heterogeneous, multi-scale, dynamic and timely information generated during the course of treatment and providing a unified, multidisciplinary, multi-scale holistic view of the patient (digital patient) and its current needs
    • By the provision of a web-based Software as a Service (SaaS) collaborative environment that includes advanced intuitive visual exploratory interfaces for studying, contrasting and presenting the patient case based on a complex digital breast cancer patient model.
  • Exploiting novel sources of information not used or underexploited clinical practice, which may have important diagnostic or prognostic value and may influence decision, such as imaging, genetic and biological data, data on administration of therapeutics, risk factors or environmental or social aspects
    • By developing a complex digital breast cancer patient (DBCP) model representing the case, that incorporates all this data in a structured manner for agile exploration and case representation, as well as intuitive data mining and visualization tools capable of retrieving and comparing similar cases and test the influence of some of these parameters on retrospective data, assessing its potential decisional value and influence.
  • Exploiting the rich information contained in routine imaging examinations providing useful and objective quantitative imaging biomarkers with prognostic and diagnostic value. These may be calculated and compared along the treatment course and also across different cases, leveraging the data accumulated in retrospective cases with known outcomes
    • By the development of highly automated advanced medical image-analysis algorithms, which will be incorporated into web-based medical interactive image analysis tools or as cloud-based batch processes.
  • Developing tools for the visual assessment of the possible aesthetic outcome of Breast Conservative Therapy, improving the interaction between the patient and the surgeon, and with possible implications in the reduction of secondary interventions or in prognostic effects of adjuvant therapies, such as radiotherapy or systemic treatments
    • By incorporating, adapting and refining an existing patient-specific multi-scale physiological model of BCT that couples a biological model of wound healing to a mechanical model of the breast tissues, and which may also incorporate the effects of systemic and RT treatments.
  • Provide decision support for the diversity of therapeutic options available in PBC (surgical options, RT treatments, (neo) adjuvant systemic therapies)
    • By developing a decision support system (DSS) based on a complex know-ledge model that evolves by incorporating the experience of the clinical team on previous cases, decisions and outcomes (represented by the DBCP data model), predictions about therapeutic outcomes (i.e. from the models) and the opinion and context information of the patient.


In order to develop this system, a multidisciplinary consortium with complementary expertise has gathered around this idea. On one hand, the consortium includes technical experts in image analysis (Ulster University, Vicomtech-IK4, ARIVIS, ERESA), biology and genetics (Sistemas Genómicos, ERESA), predictive modelling (University of Houston, Medical Innovation and Technology, Sistemas Genómicos), decision support systems (Vicomtech-IK4, INSERM-LIMICS, Ulster University), visualization (ARIVIS, Vicomtech-IK4, Ulster University) and software development in the clinical domain (BILBOMATICA, ARIVIS).

On the other hand, it includes experts in all fields related to oncology, including end users with expertise in health technology assessment and validation for radiology (ERESA), decision support systems (Hôpital Tenon, Onkologikoa, ERESA) and breast surgery (University of Houston).