Community Research and Development Information Service - CORDIS

H2020

BD2Decide Report Summary

Project ID: 689715
Funded under: H2020-EU.3.1.

Periodic Reporting for period 1 - BD2Decide (Big Data and models for personalized Head and Neck Cancer Decision support)

Reporting period: 2016-01-01 to 2017-11-30

Summary of the context and overall objectives of the project

Cancers of the Head and Neck region (HNC) are the 6th most deadly tumors worldwide, with ~630.000 newly detected cases (150.000 in Europe) and ~350.000 deaths every year (~70,000 in Europe). These cancers are now increasing especially in the younger population and constitute a significant cause of mortality. Approximately two third of HNC are detected at advanced stage (Stage III and IV); their treatment requires multimodal interventions, with a curability rate of approximately 60%, 27%-50% of cases usually relapse within two years after treatment, and salvage surgery or re-irradiation are ineffective in the majority of cases. Treatment of HNC is extremely invasive and impairing causing serious side effects to patients and treatment selection is a critical phase of patients' management. Currently patients treatment is decided based on the Tumor-Lymph-nodes-Metastases system, which considers only a few prognostic factors, not sufficient to personalize treatment. Physicians need a more accurate stratification of patients according to prognostic biomarkers able to predict the individual patient’s clinical outcome at the time diagnosis.
BD2Decide answers to these needs: the project realizes and validates a Clinical Decision Support System linking together population-specific epidemiology, behavioral and environmental data, patient-specific multiscale data from genomics, pathology, clinical and imaging data with available multiscale prognostic models and Graphical Visualization tools that allow to:
1. Improve the clinical decision process, by implementing a model-based prognostic system, expected to increase the accuracy of current TNM staging by 10%.
2. Validate the prognostic models in different populations .
3. Uncover patient-specific and population-related patterns that can improve care and patients' quality of life, and pave the way for better tailored treatment guidelines.
4. Reinforce the multidisciplinary decision-making process and patient's co-decision through advanced data visualization and presentation.
5. Create a virtuous circle of learning between research and clinical practice, through the the creation of a large and shared data repository for HNC.

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

From the clinical side, the clinical study protocol was agreed, submitted to ethics committees of participating hospitals for approval, approved and entered into force as of month 3. Ethics aspects related to patients' data privacy and security were also addressed, in compliance with EU regulations and with more strict regulations at hospital level. The clinical study has been registered in clinicaltrials.gov website. Data, images and biologic specimens for 650 retrospective and >150 prospective patients have been collected and are being processed and analyzed; patients selection and data collection is ongoing. Population data has been collected and available for analysis. HPV study on oropharynx (N=1200) oral cavity (N>1000) cancer cases was conducted at VUmc and preliminary results are available.
Users needs and scenarios have been defined, the system architecture consolidated and the first modules realized, tested and integrated in the Big Data Infrastructure established at UNIPR.
In WP3 Fraunhofer released the complete Image Analysis Tool Suite that allows the semi-automatic segmentation of tumor and lymph-nodes, the extraction of morphological features from CTs and MRIs. POLIMI and MAASTRO have completed the development and test of their radiomic software tools and have started the extraction and validation of radiomic features and signatures. In WP4 VUmc has started the recalibration of models using available data and has provided first results. In WP5 the Patients Documentation System has been realized based on open source tool OpenClinica, and the visualization and data presentation prototype tools have been completed and are being validated by end users. MAASTRO has completed the Interactive Patients co-Decision aid tool for larynx cancer in Dutch and English language; translation into German is being addressed. UPM developed the BD2Decide ontology and the Visual Analytics Tools for data query and simulations. In WP6 AII realized the Big Data Infrastructure and developed a first prototype of Big Data Analysis tools, providing first results that are now validated by users. In WP8 the technical validation is in advanced stage and the health technology assessment has started. Dissemination (WP9) activities in the period allowed to reach more than 1.800 researchers in the scientific community, 35 experts in industry, 20 representatives of civil society, 15 policy makers, 5 investors, 10 media, and more than 5.000 citizens in the general public. The project logo, website and presence in social media was established in the first months of the project. The website had 1640 visits from 1421 users. A preliminary market and technology survey (D9.9) was issued. 24 official deliverables were submitted.
Overall the Consortium has achieved all the goals and results expected in the reporting period.

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)

"The main innovations brought by BD2Decide vs. existing clinical decision support systems can be summarized in five main achievements:
1. The introduction of radiomics into the diagnostic and treatment decision process and into prognostic models.
2. The integration of different models, their recalibration considering all the multiscale factors involved in HNC progression and on population data.
3. The application of cloud computing and big data analysis techniques to uncover unexpected prognostic indicators.
4. The introduction of new collaborative concepts in patients' management: collaborative decision making through data sharing, prognostic modeling and data investigation.
5. The implementation of a ""transparent"" data presentation and prognostic modeling output.
The major results expected from the Project consist in the implementation of the following components:
• Big Data Infrastructure
• Big Data Analytics and Visual Analytics Tool.
• Clinical Decision Support System Tool and Knowledge Management System.
• BD2Decide Ontology.
• Library of Statistical models.
• Images segmentation tool, CT and MRI radiomic feature extractor tools and Phenotipization tool to extract radiomic signatures from MRIs.
• Interactive Patients' co-Decision Aid.

BD2Decide work entails relevant impact in several directions:
• Improve the clinical decision process for patients diagnosed with HNC and by creating a virtuous circle of learning between research and clinical practice, through the mutual feeding of multiple data categories.
• Demonstrate and exploit the value potential of big data for prognostic prediction, to discovery personalized prognostic patterns for HNC that outperform the currently used TNM staging system
• By improving the clinical decision making process and by involving the patients in such process, BD2Decide will have crucial impact on the way trade-off between clinical effectiveness and patients Quality of Life (QoL) is currently managed.
• Through more accurate and personalized prognosis BD2Decide is also expected to contribute to costs saving in the treatment of HNC, which currently requires to several tens of thousands of Euros per patient. This is achieved by supporting the selection of the best possible treatment options – consistent with up-to-date clinical knowledge on one side, and patients’ preferences on the other side – and by avoiding unnecessary care.
"

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