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| | text = This is my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p> | | | text = This is my primary sandbox page, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p> |
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| ==Sandbox begins below== | | ==Sandbox begins below== |
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| {{Infobox journal article
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| |title_full = How big data, comparative effectiveness research, and rapid-learning health care<br />systems can transform patient care in radiation oncology
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| |journal = ''Frontiers in Oncology''
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| |authors = Sanders, Jason C.; Showalter, Timothy N.
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| |affiliations = University of Virginia School of Medicine
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| |contact = Email: tns3b@virginia.edu
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| |editors = Deng, Jun
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| |pub_year = 2018
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| |vol_iss = '''8'''
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| |pages = 155
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| |doi = [http://10.3389/fonc.2018.00155 10.3389/fonc.2018.00155]
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| |issn = 2234-943X
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| |license = [http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International]
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| |website = [https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/full https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/full]
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| |download = [https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/pdf https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/pdf] (PDF)
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| | text = This article should not be considered complete until this message box has been removed. This is a work in progress.
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| ==Introduction==
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| Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality. We synthesize recent advances in [[genomics]] with trends in big data to provide a forward-looking perspective on the potential of new advances to usher in an era of personalized radiation therapy, with emphases on the power of RLHCS to accelerate discovery and the future of individualized radiation treatment planning.
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| ==Comparative effectiveness research (CER) and big data==
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| The Committee on CER Prioritization was created by the Institute of Medicine in 2009. They defined CER as “a strategy that focuses on the practical comparison of two or more health intervention to discern what works best for which patients and populations.”<ref name="IoMInitial09">{{cite book |url=https://www.nap.edu/catalog/12648/initial-national-priorities-for-comparative-effectiveness-research |title=Initial National Priorities for Comparative Effectiveness Research |author=Institute of Medicine of the National Academies |publisher=National Academies Press |year=2009 |isbn=9780309138369}}</ref> In essence, the goal of CER is to identify "which treatment will work best, in which patient, under what circumstances.”<ref name="GreenfieldWelcome12">{{cite journal |title=Welcome to the Journal of Comparative Effectiveness Research |journal=Journal of Comparative Effectiveness Research |author=Greenfield, S.; Rich, E. |volume=1 |issue=1 |pages=1–3 |year=2012 |doi=10.2217/cer.11.13 |pmid=24237290}}</ref> Big data refers to data sets that are so large that they cannot be analyzed directly by individuals or traditional processing software. Big data analytics (BDA) is a growing field with a multitude of methods that is being utilized in various sectors from business to medicine.<ref name="SivarajahCritical17">{{cite journal |title=Critical analysis of Big Data challenges and analytical methods |journal=Journal of Business Research |author=Sivarajah, U.; Kamal, M.M.; Irani, Z.; Weerakkody, V. |volume=70 |pages=263–86 |year=2017 |doi=10.1016/j.jbusres.2016.08.001}}</ref> The advent of the [[electronic medical record]] (EMR) has resulted in the digitization of massive data sets of medical information, including clinic encounters, [[laboratory]] values, imaging data sets and reports, pathology reports, patient outcomes, and family history, as well as genomic and biological data, etc.
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| To help with the [[Data analysis|analysis]] of big data, the [[National Institutes of Health]] (NIH) has created the Big Data to Knowledge (BD2K) program, which has invested over $200 million in grant awards to foster the development of methods and tools to analyze big data in biomedical research.<ref name="MargolisTheNat14">{{cite journal |title=The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: Capitalizing on biomedical big data |journal=JAMIA |author=Margolis, R.; Derr, L.; Dunn, M. et al. |volume=21 |issue=6 |pages=957–8 |year=2014 |doi=10.1136/amiajnl-2014-002974 |pmid=25008006 |pmc=PMC4215061}}</ref> Additionally, the BD2K program will move to make sure that biomedical big data is “findable, accessible, interoperable, and reusable” (FAIR).<ref name="MargolisTheNat14" /> Over the past decade, CER methodologies have become increasingly prevalent in radiation oncology research, and there is much enthusiasm surrounding BDA.
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| ==Rapid-learning health care system (RLHCS) and personalized medicine==
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| The number of articles on big data in health care has increased exponentially from under 500 articles in 2005 to over 2500 articles in 2015.<ref name="deLaTorreDíezBig16">{{cite journal |title=Big Data in Health: a Literature Review from the Year 2005 |journal=Journal of Medical Systems |author=de la Torre Díez, I.; Cosgava, H.M.; Garcia-Zapirain, B.; López-Coronado, M. |volume=40 |issue=9 |pages=209 |year=2016 |doi=10.1007/s10916-016-0565-7 |pmid=27520614}}</ref> As the amount of biomedical big data and our ability to analyze these data continues to advance, so will the implications and use of the [[information]] we are able to extract. One of the most important steps toward advancing our ability to analyze these big data for biomedical discovery is the creation of RLHCS, which will allow for the sharing of patient data between EMRs, ideally in real-time.<ref name="GinsburgCompar12">{{cite journal |title=Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: A common bond |journal=Journal of Clinical Oncology |author=Ginsburg, G.S.; Kuderer, N.M. |volume=30 |issue=34 |pages=4233-42 |year=2012 |doi=10.1200/JCO.2012.42.6114 |pmid=23071236 |pmc=PMC3504328}}</ref> An ideal RLHCS would take patient data that was routinely generated as part of standard patient care and compile that data into a large data system.<ref name="GinsburgCompar12" /><ref name="GinsburgAcademic11">{{cite journal |title=Academic medical centers: Ripe for rapid-learning personalized health care |journal=Science Translational Medicine |author=Ginsburg, G.S.; Staples, J.; Abernethy, A.P. |volume=3 |issue=101 |pages=101cm27 |year=2011 |doi=10.1126/scitranslmed.3002386 |pmid=21937754}}</ref><ref name="AbernathyRapid10">{{cite journal |title=Rapid-learning system for cancer care |journal=Journal of Clinical Oncology |author=Abernethy, A.P.; Etheredge, L.M.; Ganz, P.A. et al. |volume=28 |issue=27 |pages=4268-74 |year=2010 |doi=10.1200/JCO.2010.28.5478 |pmid=20585094 |pmc=PMC2953977}}</ref> This aggregate data would then be available for both BDA to accelerate identification of new hypotheses and CER to rapidly generate evidence through hypothesis-testing studies. Clinical data from patient records can be used readily to identify novel relationships among clinical factors and patient outcomes, or to evaluate treatment effectiveness in specific subgroups, that cannot be studied adequately in randomized, controlled trials. The extreme power of RLCHS, though, is even more exciting when one considers the possibility of adding biospecimens to accelerate discovery in genomics and proteomics. As RLHCSs are created and their data sets are expanded, we will continue to identify specific genomic and proteomic data to help define cohorts and stratify patients into risk groups and treatment response groups, and potentially to help design highly tailored therapy regimens.<ref name="RamseyHow11">{{cite journal |title=How comparative effectiveness research can help advance 'personalized medicine' in cancer treatment |journal=Health Affairs |author=Ramsey, S.D.; Veenstra, D.; Tunis, S.R. et al. |volume=30 |issue=12 |pages=2259–68 |year=2011 |doi=10.1377/hlthaff.2010.0637 |pmid=22147853 |pmc=PMC3477796}}</ref> In this sense, the RLHCS would usher in a more fertile era for improving biomedical research than ever before. BDA and CER provide the research methodologies needed to rapidly generate evidence from the RLHCS. It should be noted, however, that there are substantial practical obstacles that must be addressed to achieve the vision of the RLHCS. These include patient concerns regarding privacy and security of sensitive information, interconnectivity among different health records, and regulatory barriers to the exchange of health information.
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| ==Integrating a RLHCS with oncology==
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| The integration of CER, big data, and BDA is especially important in the field of oncology, where multiple groups are investing significant time and resources in efforts to expand the availability of data and advance the methods used to extract meaningful information from that data.<ref name="MargolisTheNat14" /><ref name="HelftCanBig14">{{cite journal |title=Can big data cure cancer? |journal=Fortune |author=Helft, M. |volume=170 |issue=2 |pages=70–4, 76, 78 |year=2014 |pmid=25318238}}</ref><ref name="WilliamsArtificial18">{{cite journal |title=Artificial intelligence, physiological genomics, and precision medicine |journal=Physiological Genomics |author=Williams, A.M.; Liu, Y.; Regner, K.R. et al. |volume=50 |issue=4 |pages=237–43 |year=2018 |doi=10.1152/physiolgenomics.00119.2017 |pmid=29373082 |pmc=PMC5966805}}</ref><ref name="SavageBigData14">{{cite journal |title=Big data versus the big C |journal=Scientific American |author=Savage, N. |volume=311 |issue=1 |pages=S20–1 |year=2014 |pmid=24974705}}</ref><ref name="ShahBuilding16">{{cite journal |title=Building a rapid learning health care system for oncology: Why CancerLinQ collects identifiable health information to achieve its vision |journal=Journal of Clinical Oncology |author=Shah, A.; Stewart, A.K.; Kolacevski, A. et al. |volume=34 |issue=7 |pages=756–63 |year=2016 |doi=10.1200/JCO.2015.65.0598 |pmid=26755519}}</ref><ref name="TrifilettiBigData15">{{cite journal |title=Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery |journal=Frontiers in Oncology |author=Trifiletti, D.M.; Showalter, T.N. |volume=5 |pages=274 |year=2015 |doi=10.3389/fonc.2015.00274 |pmid=26697409 |pmc=PMC4672039}}</ref> The American Society of Clinical Oncology started their own RLHCS, CancerLinQ, to overcome the lack of interoperability between EMRs and accomplish their goal of being able to “analyze and share data on every patient with cancer.”<ref name="ASCOShaping11">{{cite web |url=https://www.asco.org/sites/default/files/shapingfuture-lowres.pdf |title=Shaping the Future of Oncology: Envisioning Cancer Care in 2030: Outcomes of the ASCO Board of Directors Strategic Planning and Visioning Process, 2011-2012 |publisher=American Society of Clinical Oncology |date=2011}}</ref> While the vision of RLCHS has not yet been fully achieved, the potential impact on society has stimulated enthusiasm toward this effort.
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| ==Implications for radiation oncology==
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| ===Patient reported outcomes (PROs)===
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| Patient reported outcomes and quality-of-life (QoL) have become a major area of focus in health care overall, particularly in oncology. The availability of PROs within EMRs provides the foundation for a RLHCS that can be leveraged to expand insights into how cancer treatments impact patient QoL. By incorporating the PROs for massive numbers of patients, RLHCS will be able to identify small variations and subgroups of patients that might be missed in the smaller number of patients included in traditional randomized controlled trials. These PROs and QoL domains can then be incorporated into clinical decision-making to help guide both providers and patients.<ref name="SarinBigData14">{{cite journal |title=Big Data V4 for integrating patient reported outcomes and quality-of-life indices in clinical practice |journal=Journal of Cancer Research and Therapies |author=Sarin, R. |volume=10 |issue=3 |pages=453-5 |year=2014 |doi=10.4103/0973-1482.142741 |pmid=25313720}}</ref> In doing this, PROs can act as a link between the objective clinical data and the subjective patient outcomes and experiences to help improve the overall care of the patient.<ref name="KimPredict17">{{cite journal |title=Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: A preliminary study |journal=Journal of Radiotherapy in Practice |author=Kim, K.H.; Lee, S.; Shim, J.B. et al. |volume=16 |issue=2 |pages=161–70 |year=2017 |doi=10.1017/S1460396916000583}}</ref> One may also conceive of potential genomics-based determinants of QoL that could be identified using BDA if RLHCSs include biospecimens linked to clinical data and PROs. Finally, surveillance of an RLHCS may also be performed to identify temporal trends in PROs to estimate outcomes after implementation of new technologies.
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| ===Dose selection and radiosensitivity===
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| The use of tumor-specific genes and radiosensitivity to guided treatment decisions has already been established in human papilloma virus-associated squamous-cell carcinoma of the oropharynx.<ref name="ChenReduced17">{{cite journal |title=Reduced-dose radiotherapy for human papillomavirus-associated squamous-cell carcinoma of the oropharynx: A single-arm, phase 2 study |journal=The Lancet, Oncology |author=Chen, A.M.; Felix, C.; Wang, P.C. et al. |volume=18 |issue=6 |pages=803–11 |year=2017 |doi=10.1016/S1470-2045(17)30246-2 |pmid=28434660}}</ref> Numerous studies have looked at identifying genes that may have implications on tumor radiosensitivity or patient toxicity.<ref name="WestGenetics11">{{cite journal |title=Genetics and genomics of radiotherapy toxicity: Towards prediction |journal=Genome Medicine |author=West, C.M.; Barnett, G.C. |volume=3 |issue=8 |pages=52 |year=2011 |doi=10.1186/gm268 |pmid=21861849 |pmc=PMC3238178}}</ref><ref name="Torres-RocaPredict05">{{cite journal |title=Prediction of radiation sensitivity using a gene expression classifier |journal=Cancer Research |author=Torres-Roca, J.F.; Eschrich, S.; Zhao, H. et al. |volume=65 |issue=16 |pages=7169-76 |year=2005 |doi=10.1158/0008-5472.CAN-05-0656 |pmid=16103067}}</ref><ref name="ChistiakovGenetic08">{{cite journal |title=Genetic variations in DNA repair genes, radiosensitivity to cancer and susceptibility to acute tissue reactions in radiotherapy-treated cancer patients |journal=Acta Oncologica |author=Chistiakov, D.A.; Voronova, N.V.; Chistiakov, P.A. |volume=47 |issue=5 |pages=809-24 |year=2008 |doi=10.1080/02841860801885969 |pmid=18568480}}</ref><ref name="EschrichAGene09">{{cite journal |title=A gene expression model of intrinsic tumor radiosensitivity: Prediction of response and prognosis after chemoradiation |journal=International Journal of Radiation Oncology, Biology, and Physics |author=Eschrich, S.A.; Pramana, J.; Zhang, H. et al. |volume=75 |issue=2 |pages=489-96 |year=2009 |doi=10.1016/j.ijrobp.2009.06.014 |pmid=19735873 |pmc=PMC3038688}}</ref> The identification of these genes and their potential implications has led to the creation of the fields of radiogenetics and radiogenomics. Efforts are currently underway to generate meaningful gene assays that will help predict tumor response to radiation. Eschrich ''et al.'' created a 10-gene model to calculate a radiosensitivity index and applied this to patients with head-and-neck, rectal, and esophageal cancer to help stratify patients into either responders or non-responders, with 80% sensitivity and 82% specificity.<ref name="EschrichAGene09" /> Similarly, Zhao ''et al.'' retrospectively created a 24-gene assay and applied this to risk matched patients who either received postoperative radiation or no radiation following prostatectomy. Patients with a high score on the gene index who received postoperative radiation were less likely to have distant metastasis at 10 years.<ref name="ZhaoDevelop16">{{cite journal |title=Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: A matched, retrospective analysis |journal=The Lancet, Oncology |author=Zhao, S.G.; Chang, S.L.; Spratt, D.E. et al. |volume=17 |issue=11 |pages=1612–20 |year=2016 |doi=10.1016/S1470-2045(16)30491-0 |pmid=27743920}}</ref>
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| As efforts to identify genes and gene assays that may be predictors of radiosensitivity continue to be validated, we will potentially be able to integrate these findings in dose selection and toxicity prediction for individual patients based on their native and tumor genetics. Scott and colleagues have recently described a genomics-based strategy for personalizing radiation therapy dose, which would support dose de-escalation for radiosensitive tumors.<ref name="ScottAGenome17">{{cite journal |title=A genome-based model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study |journal=The Lancet, Oncology |author=Scott, J.G.; Berglund, A.; Schell, M.J. et al. |volume=18 |issue=2 |pages=202-211 |year=2017 |doi=10.1016/S1470-2045(16)30648-9 |pmid=27993569}}</ref> While the clinical implication of radiosensitivity assays are still developing, big data will be key to developing future assays rapidly, as well as incorporating the genomics tools into clinical decision-making. Big data provides an opportunity to refine molecular signatures based upon real-world data and to merge genomic assay results with other clinical data elements to optimize predictive analytics. An RLHCS would provide the ideal substrate for levering big data and CER to accelerate genomics-based discovery to make precision radiation oncology a reality.
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| ==References==
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| {{Reflist|colwidth=30em}}
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| ==Notes==
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| This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
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| <!--Place all category tags here-->
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| [[Category:LIMSwiki journal articles (added in 2018)]]
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| [[Category:LIMSwiki journal articles (all)]]
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| [[Category:LIMSwiki journal articles on big data]]
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| [[Category:LIMSwiki journal articles on health informatics]]
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