Creating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets

Abstract

Disconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within “Big Data”. Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology.

Prospective review of radiotherapy trials through implementation of standardized multicentre workflow and IT infrastructure

“…Recently, Skripcak et al 16 reported on challenges and possible solutions for the strategic development of international research data exchange framework in radiation therapy and oncology and identified three major classes of data-pooling models: centralized, decentralized and hybrid. Our approach used a decentralized model in which data are collected, validated and processed at the designated RTTQA centre by expert trial QA staff.…”

Intelligence artificielle appliquée à la radiothérapie

“…De nouvelles approches sont donc nécessaires afin de générer des études de bon niveau de preuve, et l'une de ces approches est la constitution et l'exploitation de bases de données massives détaillées, intégrant un grand nombre de paramètres [3]. Au sein de ces données, la biologie moléculaire et la génomique sont appelées à jouer un rôle prépondérant.…”

Big Data in Radiation Oncology: Challenges and Opportunities

“…As indicated in a recent study, radiation oncology specialty is a top offender for exceeding the sustainable growth rate (SGR) target, with its excess expenditures from 2003 to 2009 hovering at 300% range as compared to 2002 [8]. To mitigate negative impact of Medicare cuts on cancer patents and radiation oncology specialty, big data can be exploited to improve treatment efficacy, optimize therapeutic ratio as well as personalize treatment options [9,10]. Finally, patient data generated within radiation oncology practices is currently residing in separated silos, and has so far not translated into some actionable insights into the best ways of treating patients who are the source of these data.…”

“…Finally, patient data generated within radiation oncology practices is currently residing in separated silos, and has so far not translated into some actionable insights into the best ways of treating patients who are the source of these data. If such big data can be assembled efficiently and analyzed systematically, great statistical power derived from these largescale population-based analyses can be leveraged to assist in clinical decision making and prediction of treatment outcomes [9,10].…”

“…Nevertheless, there is a strong urgency in utilizing big data on a standardized database derived from big data contributed by multiple institutions [9,10]. Furthermore, radiobiologists can gain insights into treatment response modeling and outcome prediction with the help of big data.…”