Community Research and Development Information Service - CORDIS

H2020

DESIREE Report Summary

Project ID: 690238
Funded under: H2020-EU.3.1.

Periodic Reporting for period 1 - DESIREE (Decision Support and Information Management System for Breast Cancer)

Reporting period: 2016-02-01 to 2017-07-31

Summary of the context and overall objectives of the project

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. BUs consists of a multidisciplinary team of clinicians, including medical oncologists, surgeons, radiologists, radioncologists, pathologists and other profiles that periodically meet to discuss new and ongoing cases in order to take therapeutic decisions.

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. Medical images may consist of complex datasets, sometimes including 3D modalities such as Magnetic Resonance Imaging with increasing resolution and different sequences providing complementary information. Digital pathology slides of tumor samples are starting to be commonplace and the advent of the massive sequencing era, is starting to provide crucial information allowing to characterize the tumor (tumoromics) or the possible reaction of the patient to the drug (pharmacogenomics). The potential of exploiting this 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.

Furthermore, clinicians have to keep up-to- date with an overwhelming amount of studies, evidence and new therapeutic options. What is worse, clinical guidelines, based on strong evidence, may be months or years behind the state-of- the-art in diagnostic and treatments that already impact every day care. Still, there are many gaps regarding the applicability of a specific treatment on a given patient, as clinical trials only represent a limited spectrum of the population the drug is targeting. Clinicians in the BU have to deal with all this information during case presentation, creating a picture of the patient and a mental map of knowledge, and take a therapeutic decision, sometimes clear, sometimes uncertain, in a time span that may range from 3 to 10 minutes, most of the times with the only help of a few pictures.

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 will come 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, we aim to develop a set of prognostic imaging biomarkers and ultimate real time visualizations valed on image analysis that provide insight into the disease and its progression and may help decision making. Last, but not least, we aim to provide 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 mi

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Coordination and Management activities are taking place in WP1, very importantly including different types of reports and continuous virtual and face-to-face meetings.

The work carried out during WP2 EXPLORE, allowed to set the grounds for the overall system definition and design, including an initial software architecture.

Work in WP3 is steadily progressing towards the characterization of the tissue and tumor both in mammographic and magnetic resonance images.

WP4 is progressing towards the incorporation of an initial Breast Conservative Therapy (BCT) physiological model that is being integrated into the web-based tool by moving the current developments from a research environment. Furthermore, a radiobiological model is being develop to predict the outcomes of radiotherapy treatments.

The core of the Decision Support System is being developed in WP5, where a knowledge model serves as the base for the decisional engine providing the recommendations.

The aforementioned functionalities are implemented through highly exploratory visual interfaces that allow to introduce and explore patients cases (WP6). A large effort of integration is taking place.

Progress of the validation WP7 is mostly limited to technical validation of the software pieces being develop.

In WP8 Dissemination and Exploitation, a set of different important dissemination actions have taken place.

NOTE: For further information, please kindly check the Periodic Report

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We have already set the grounds for the first known decision support system operating in a breast unit. The system is able to introduce and represent real patient cases and the reasoning engine behind the three DSS subsystems (guideline, experince and case-based DSS) is already being demostrated in initial prototypes. This already includes well-designed highly explorative and intuitive visual interfaces incorporating novel paradigms for case review.

The imaging system is steadily progressing. A first outline and prototype implementation for real-time augmented image visualization during case-review is almost complete. This approach differs from current image-based softwares more focused on diagnostic by radiologist than on case review and exploration by the BUs. More work is needed in order to train and fine-tune image analysis algorithms that will add more added value to these initial visualizations. We hope that the power of multi-modality imaging will start to be demonstrated in the following months.

Most of the work being done in the physiological modelling part is already beyond the state of the art, as this is a pioneering work in terms of predictive visualization of multi-scale physiological processes after breast conservative therapy. We expect also the radiobiological models to provide novel insight that may help in radiotherapy treatment planning.

NOTE: For further information, please kindly check the Periodic Report

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