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• W2341775483 abstract "Awareness of patient-reported outcomes (PROs) as vital elements for big data efforts to incorporate measures of patients' quality of life (QOL) into the analysis of efficacy of care is emerging ( 1 Abernethy A.P. Coeytauz R. Rowe K. et al. Electronic patient-reported data capture as the foundation of a learning health care system. J Clin Oncol. 2009; 27 ([abstract]): 15s Crossref Scopus (16) Google Scholar , 2 Sloan J.A. Berk L. Roscoe J. et al. Integrating patient-reported outcomes into cancer symptom management clinical trials supported by the National Cancer Institute-sponsored clinical trials networks. J Clin Oncol. 2007; 25: 5070-5077 Crossref PubMed Scopus (84) Google Scholar ). PRO domains can include functional status, symptoms (intensity, frequency), satisfaction (with medication), multiple domains of well-being, and global satisfaction with life. Today, either virtually all validity issues for PROs have been resolved or clear guidelines have been established ( 3 Halyard M.Y. Frost M.H. Dueck A. et al. Integrating QOL assessments for clinical and research purposes. Curr Probl Cancer. 2006; 30: 319-330 Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar , 4 Brundage M. Blazeby J. Revicki D. et al. Patient-reported outcomes in randomized clinical trials: Development of ISOQOL reporting standards. Qual Life Res. 2013; 22: 1161-1175 Crossref PubMed Scopus (139) Google Scholar , 5 Sprangers M.A. Sloan J.A. Barsevick A. et al. Scientific imperatives, clinical implications, and theoretical underpinnings for the investigation of the relationship between genetic variables and patient-reported quality-of-life outcomes. Qual Life Res. 2010; 19: 1395-1403 Crossref PubMed Scopus (37) Google Scholar ). 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 DataInternational Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewBig 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). Full-Text PDF Needs and Challenges for Big Data in Radiation OncologyInternational Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewThe 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. Full-Text PDF Impending Challenges for the Use of Big DataInternational Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewAdvances 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). Full-Text PDF How Will Big Data Improve Clinical and Basic Research in Radiation Therapy?International Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewAdvances 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 CareInternational Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewRadiation 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. Full-Text PDF How Will Big Data Impact Clinical Decision Making and Precision Medicine in Radiation Therapy?International Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3Preview“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). Full-Text PDF How Can We Effect Culture Change Toward Data-Driven Medicine?International Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewWe are in the midst of an explosion of expanding quantitative data that can be measured or derived for each patient: quantitative imaging and its younger sibling, radiomics; tumor and normal tissue genomics and proteomics; electronic health record data and treatment notes; dose distribution treatment plans and imaging used for treatment guidance; and various outcomes measures, including, for example, patient-reported outcomes. However, we are struggling to meet the promise of this expansion. Full-Text PDF A Systems Approach Using Big Data to Improve Safety and Quality in Radiation OncologyInternational Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewRadiation 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. Full-Text PDF Reality CheckInternational Journal of Radiation Oncology, Biology, PhysicsVol. 95Issue 3PreviewThere 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. Full-Text PDF Erratum to: Sloan JA, Halyard M, El Naqa I, Mayo C. Lessons From Large-Scale Collection of Patient-Reported Outcomes: Implications for Big Data Aggregation and Analytics. Int J Radiat Oncol Biol Phys 2016;95:922-929.International Journal of Radiation Oncology, Biology, PhysicsVol. 105Issue 4PreviewResearch reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Numbers UG1CA189823 and U10CA180821 (to the Alliance for Clinical Trials in Oncology), UG1CA189808 , UG1CA189812 , U10 CA035415 , and U10 CA035103 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 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