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Big Data and models for personalized Head and Neck Cancer Decision support

Periodic Reporting for period 3 - BD2Decide (Big Data and models for personalized Head and Neck Cancer Decision support)

Período documentado: 2018-12-01 hasta 2019-09-30

Cancers of the Head and Neck region (HNC) are the 6th most deadly tumors worldwide, with ~630.000 newly detected cases and ~350.000 deaths every year. These cancers are 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; treatment selection is a critical phase of patients' management. Currently patients’ treatment is decided based on the Tumor-Lymph-Nodes-Metastases system (TNM), which considers only a few prognostic factors, not sufficient to accurately discriminate patients into high- and low-risk and to personalize treatment. Accurate stratification of patients able to predict at the time diagnosis the individual patient’s clinical outcome is highly needed. BD2Decide has realized and validated prognostic models and signatures 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, through a model-based clinical decision support system that increases TNM prognostic accuracy by 10%
2. Identify patient- and population-specific prognostic factors, thus informing personalized interventions and paving the way for new treatment guidelines
3. Reinforce the multidisciplinary decision-making process and patient's co-decision through advanced data visualization and presentation.
4. Produce a virtuous circle of learning between research and clinical practice, through the creation of a large and shared data repository for HNC.
BD2Decide will inform personalized treatment decisions that would avoid unnecessary invasive therapies, induced toxicities and side effects for patients at low risk while increasing surveillance and intensive treatment to patients identified as high-risk by our prognostic models. The final goal is to generate new knowledge for physicians and researchers informing treatment decisions with positive impacts on patients' survival and quality of life.
The clinical study approved by ethics committees and registered in of participating hospitals is progressing. A total of 1086 retrospective and 451 prospective patients have been enrolled for whom clinical/medical, pathology, treatment, toxicity and follow-up data have been collected, along with diagnostic images and genomic profiles extracted from relevant biological specimens. These data have been used to validate already published prognostic models and related signatures and to validate a newly identified genomic signature for oral cavity cancer (to be patented). Population data (cancer registries for 5 EU Countries and socio-economic, lifestyle, environmental data) have been collected and used for model recalibration and big data analysis. HPV study on oropharynx (N=1200) oral cavity (N>1000) cancer cases was conducted at VUMC leading to a new method for HPV analysis. Software for features extraction and semi-automatic tumor delineation in CT and MRI scans has been produced and successfully used during the project. Certification pathways for the developed tools were defined. A co-decision aid involving patients in treatment decisions has been produced in Dutch, English and German language for challenging larynx cancer and verified by physicians, patients and ethics experts. The clinical assessment has shown that BD2Decide prognostic algorithms improve TNM at least by 10% for HNC. Effects on quality of life were also analyzed showing possible improvements. A complete ontology for HNC data annotation was developed and implemented. A Visual Analytics Tools for data query and simulations is available for data analysis and research. A secure and privacy-preserving Big Data Infrastructure has been deployed to store and manage all the collected data and images. Large dissemination was conducted with 43 published papers, 4 conferences and 3 workshops organized, reaching more than 17000 researchers in the scientific community, more than 1000 industrial representatives, more than 4000 representatives of the civil society, 205 policy makers, 45 investors, 105 media, and more than 50.000 citizens in the general public. The project logo, website and presence in social media was established in the first months of the project. From the project start the website had a 4426 visits from 2805 users. The prospect market has been sized, IPRs have been defined, exploitation intentions defined. The project achieved all the expected objectives and realized the largest HNC dataset in Europe.
"The main innovations brought by BD2Decide can be summarized in five main achievements:
1. The introduction of radiomics and combined genomics into the diagnostic and treatment decision process and into prognostic models.
2. The integration of different models and 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 HHC subtype-specific models.
• CT and MRI image analysis and radiomic feature extractor tools for combined radio-genomic prognostic modelling.
• Interactive Patients' co-Decision Aid.
BD2Decide work entails relevant impact in several directions:
• Improve the clinical decision process for patients diagnosed with HNC by integrating existing medical knowledge with newly generated genomics, radiomic and combined multifactorial prognostic models
• Better manage complex HNC patients, such as Stage III patients classified at low-risk by TNM who present an aggressive disease or HPV-negative HNC patients for which post-surgery chemoradiation would be needed.
• Demonstrate and exploit the value potential of big data for discovering personalized prognostic patterns for HNC that outperform current TNM staging system
• By Improving collaborative clinical decision-making process through and 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 prediction and treatment decisions, consistent with up-to-date clinical knowledge and patients’ preferences, BD2Decide is expected to contribute reducing healthcare and social costs of HNC, optimizing care delivery and avoiding unnecessary care."