Breast cancer is the most common and most deadly type of cancer affecting woman in the EU countries, with more than 460,000 new cases and 130,000 deaths in 2012. Multidisciplinary Breast Units (BUs) were introduced in order to deal efficiently with breast cancer cases, setting guideline-based quality procedures, clinical decisions on cases based on consensus and a high standard of care.
Despite the evident advances, daily clinical practice and case presentation in the BUs is hampered by the complexity of the disease, the ever-growing amount of patient and disease data available in the digital era, the difficulty in coordination, the pressure exerted by the system and the difficulty in deciding on cases that guidelines do not reflect.
The amount of data generated for every case may be overwhelming. A single case usually lasts for months or years, with repeated cycles of diagnostics and treatments. The associated digital information generated is increasing exponentially. It includes non-strucuture rich information data sources, such as complex medical imaging datasets, which may include 3D modalities and genomic sequencing data providing crucial information allowing to characterize the tumor (tumoromics) or the possible reaction of the patient to the drug (pharmacogenomics) which in turn determines therapeutic strategies. The potential of exploiting this heterogeneous information and comparing it with other cases is enormous. However, the disparity of sources, inconsistency in representation and lack of tools for retrospective analysis prevent exploiting all this information in an agile manner for continuous care or discovery.
The advent of the BUs has had an important impact in oncology practice, but may drown in an intractable amount of data. This is where DESIREE comes to the rescue, by providing a set of software tools, models and processes to deal with the efficient case representation of BUs, the exploitation of rich sources of information such as imaging or genomics and the review and learning from retrospective cases. Altogether, we call this intelligent software system a Decision Support System, aiming to provide timely information and support for case review in the Breast Unit, based on clinical guidelines, experience from previous cases, advice for non-compliant decisions and exploitation of retrospective cases.
Furthermore, DESIREE develops imaging biomarkers, characterizing breast tissue and tumor and ultimate real-time visualizations based on image segmentation and analysis that provide insight into the disease and its progression, helping decision making. Last, but not least, DESIREE has developed a novel tool, based on a complex physiological model, for predicting the aesthetic long-term outcome of the Breast Conservative Therapy intervention (resection of the minimal amount of tissue necessary instead of complete mastectomy), which is useful both by the the patient and the surgeon.
Our ultimate goal, is to provide intuitive and intelligent software tools that improve the case review and management of the breast units and ultimately are capable to impact the patients by providing the necessary information to ultimately optimize treatments and outcomes.
