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Needs and Challenges for Big Data in Radiation Oncology

Published:November 27, 2015DOI:https://doi.org/10.1016/j.ijrobp.2015.11.032
      The promise of big data in medicine is based on the premise that large aggregates of data (and the increasing capacity to capture the data) will yield important insights into the real-world care of patients with the true signal rising above the noise. Large-scale analyses into routine clinical care would complement but not replace the internally valid data generated with randomized trials. Given the databases and imaging platforms that current patient care and workflows are based upon, radiation oncology lends itself quite naturally to the big data initiative. This offers the potential to leverage these standardized platforms to develop new strategies to offer personalized medicine, a continually learning health care system (Fig. 1) using machine learning strategies (
      • El Naqa I.
      • Li R.
      • Murphy M.
      Machine Learning in Radiation Oncology: Theory and Applications.
      ).
      Figure thumbnail gr1
      Fig. 1A learning health care system uses a knowledge base and machine learning to make predictions of the potential outcomes in individual patients or patient populations. The knowledge base must contain both the diagnostic and prognostic information (facts) and the eventual outcomes in prior patients. The prediction models can use the facts and any clinical variables (representing clinical options) to assist in the predictions. The overall goal is for outcomes to improve over time as the system learns with each new patient. The outcomes can be measures of disparities, quality of care, or individual patient responses to treatment.
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      Linked Article

      • Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data
        International Journal of Radiation Oncology • Biology • PhysicsVol. 95Issue 3
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          Big data research refers to the collection and analysis of large sets of data elements and interrelationships that are difficult to process with traditional methods. It can be considered a subspecialty of the medical informatics domain under data science and analytics. This approach has been used in many areas of medicine to address topics such as clinical care and quality assessment (1-3). The need for informatics research in radiation oncology emerged as an important initiative during the 2013 National Institutes of Health (NIH)–National Cancer Institute (NCI), American Society for Radiation Oncology (ASTRO), and American Association of Physicists in Medicine (AAPM) workshop on the topic “Technology for Innovation in Radiation Oncology” (4).
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      • Impending Challenges for the Use of Big Data
        International Journal of Radiation Oncology • Biology • PhysicsVol. 95Issue 3
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          Advances in data storage and data analysis materialized also in health care data. In recent years, we have seen an emphasis on using the full potential (1, 2) of these data to answer questions such as: who were the patients that received radiation therapy as primary treatment? Who among such patients experienced radiation therapy–related complications? Given everything you know about my case, what is the chance that if I choose radiation therapy, I will experience incontinence in the next year? Factors contributing to this trend include more rapid data querying technologies, cheaper data storage, addition of genomic data to traditional clinical data sets, “meaningful use incentives” for increasing the adoption of electronic health records, and recent emergence of precision medicine (3).
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      • How Will Big Data Improve Clinical and Basic Research in Radiation Therapy?
        International Journal of Radiation Oncology • Biology • PhysicsVol. 95Issue 3
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          Advances in computer storage, computing power, and statistical methods and the ability to electronically associate multiple types of data from disparate sources (eg, demographic, genetic, imaging, treatment, and outcomes) have enabled “big data” research in radiation therapy. Rather than setting a certain minimum number of computer memory bytes to define what is meant by big data, inasmuch as this threshold would be continuously increasing with technologic advances and the size of databases, it is most reasonable to refer to big data simply as volumes of large, complex, and linkable information (1, 2).
        • Full-Text
        • PDF
      • Introduction to Big Data in Radiation Oncology: Exploring Opportunities for Research, Quality Assessment, and Clinical Care
        International Journal of Radiation Oncology • Biology • PhysicsVol. 95Issue 3
        • Preview
          Radiation oncology is in the vanguard of the collection of digital and structured information about patients for use in learning and advancing care through new big data initiatives. With its ingrained data collection history, the field provides a rich and fertile environment for exploring emerging big data opportunities in cancer care and research. Radiation therapy provides a unique combination of clinical patient demographics; physical use of radiation; application of image guidance (“radiomics”); and biological markers (genomics, proteomics, metabolomics) generated over a treatment period that can span a few days to several weeks and months.
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      • How Will Big Data Impact Clinical Decision Making and Precision Medicine in Radiation Therapy?
        International Journal of Radiation Oncology • Biology • PhysicsVol. 95Issue 3
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          “Personalized” or “precision” medicine refers to medical treatment tailored to the individual characteristics of each patient. As noted by the President's Council of Advisors on Science and Technology, precision medicine involves “the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not” (1).
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      • A Systems Approach Using Big Data to Improve Safety and Quality in Radiation Oncology
        International Journal of Radiation Oncology • Biology • PhysicsVol. 95Issue 3
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          Radiation therapy is a complex sociotechnical system for the treatment of cancer and other diseases that combines hardware, software, and human operators. We have learned, unfortunately, that this complexity leads to unintentional errors. Despite the implementation of many safety procedures in the clinic, it is clear that safety gaps, such as dose delivery errors, still exist. In addition to safety, there are also quality concerns in radiation therapy, and there is much evidence that the practice of radiation therapy varies greatly even though similar technologies are used worldwide.
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        • PDF
      • Reality Check
        International Journal of Radiation Oncology • Biology • PhysicsVol. 95Issue 3
        • Preview
          There is much excitement surrounding big data research within radiation oncology. Indeed, our field is rich in quantitative data (eg, doses, volumes, images), and the prospects for harnessing these data to build better predictive models are enticing. However, there are multiple factors to temper this excitement.
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