Spots Global Cancer Trial Database for Personal Health Train for Radiation Oncology in India and The Netherlands

Study Description

Brief Summary: The primary and general objective of this protocol as the current standard of care is to improve the quality of radiotherapy for HNC patients. This will ultimately be achieved by optimizing locoregional tumour control and overall survival and by reducing radiation-induced side effects. It will also allow the assessment of the effects of newly introduced radiation technology (e.g. proton therapy) for this particular group of patients. The clinical introduction of this standard follow-up program (SFP) will allow for a systematic and broad scale quality improvement cycle for HNC patients treated with radiotherapy.

Detailed Description: "Big data analytics in cancer care holds immense potential to unlock valuable clinical insights from an abundance of patient medical records, aided by sophisticated statistical models, that will lead to improved population-based outcomes and deeper personalization of cancer treatment. However, the clinical data (which includes medical images, clinical examinations and laboratory results) has been locked away in disconnected "silos" within every clinic. Additionally, patient information is exceedingly sensitive to privacy issues and confidentiality breaches. The investigators have pioneered the innovative Personal Health Train approach, whereby support for choosing the best treatment (i.e. decision support) is accessible without any patient records ever leaving the clinic of origin. This extends our current work on an extensible data architecture to learn from quantitative imaging data in India and The Netherlands (without images being taken out of the clinic) - NWO/DeITy BIONIC. The investigators have now developed numerous models of clinical outcome after treatment, including those for undesirable side-effects of treatment. The investigators continue to lead big data integration work within multicenter clinical decision support projects such as KWF-ProTraIT and Horizon2020-BD2DECIDE. The overall aim of the TRAIN project is to combine big data (including images, laboratory tests and clinical examinations) to improve the outcomes for head \& neck cancer patients in both India and The Netherlands. The investigators will do this by creating data-driven Decision Support Systems to predict which treatment gives the best outcome given individual patient characteristics, and local diagnostic and treatment capabilities. Cancer specialists in both countries will lead the design and clinical evaluation of this decision support system, which could be deployed in multiple clinics across all of the settings encountered in India and The Netherlands. Head and neck cancer is a relatively rare condition in the Netherlands, such that the data volume available to learn from is much smaller than in India. Conversely, Indian patients typically present at a more advanced stage of cancer compared to Dutch patients. These differences in patients and treatments can be leveraged by machine learning algorithms to learn better predictive models. Decision support systems are essential, since guideline deviations in both countries are common due to individual patient characteristics, patient preferences and uneven distribution of treatment capacity outside major urban centers. To achieve the above, The investigators first deploy the ICT infrastructure (in collaboration with Philips India) to connect local hospital information systems so that clinical, imaging and outcome data on head \& neck cancer patients becomes findable, accessible, interoperable and reusable (FAIR) big data. The investigators then deploy learning algorithms that traverse the big data repositories of each participating hospital, using the privacy-preserving Personal Health Train approach, to develop a decision support system. Cancer specialists in India and the Netherlands will jointly evaluate the clinical utility of the decision support system by means of a prospective randomized clinical trial."

Keywords

Eligibility

Minimum Age: 18 Years

Eligible Ages: ADULT, OLDER_ADULT

Sex: ALL

Healthy Volunteers: No

Locations

Contact Details

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