2017 AAPM Annual Meeting
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Session Title: Big Data 2: New and Emerging Big Data Strategies in Radiation Oncology
Question 1: The risk factors that may increase a person’s chances of developing cancer include:
Reference:Danaei G, Hoorn SV, Lopez AD et al. Causes of cancer in the world: comparative risk assessment of nine behavioural and environmental risk factors, The Lancet. 2005; 366(9499):1784-1793. Tomasetti C, Vogelstein B. Cancer etiology: variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science. 2015; 347(6217):78-81. 
Choice A:Ionizing radiation to critical organs and tissues.
Choice B:Environmental conditions such as air quality and chemical absorption.
Choice C:Lifestyle pattern like smoking, alcohol drinking, and physical activity.
Choice D:Random mutations during stem cell divisions.
Choice E:All of the above.
Question 2: The main reason(s) that machine learning can be applied in cancer risk prediction is: 
Reference:Bibault J, Giraud P, Burgun A. Big Data and machine learning in radiation oncology: State of the art and future prospects, Cancer Letters. 2016; 382(1): 110-117.
Choice A:More and more patient data is accumulated in the clinic routinely and available for mining.
Choice B:Computer hardware and chip performance has been improved significantly recently.
Choice C:There are multiple carcinogenic factors entangled with hidden layers of correlations.
Choice D:All of the above.
Choice E:None of the above.
Question 3: Which V is the biggest problem for extracting big data in radiation oncology?
Reference:Mayo CS, Kessler ML, Eisbruch A, The Big Data Effort in Radiation Oncology: Data Mining or Data Farming? Advances in Radiation Oncology (2016) 1, 260-271. Lustberg T, van Soest J, Jochems A et al. Big Data in radiation therapy: challenges and opportunities. Br J Radiol. 2017 Jan;90(1069):20160689
Choice A:Variability.
Choice B:Velocity.
Choice C:Volume.
Choice D:Value.
Choice E:None of the above.
Question 4: Which factors are important for enabling incorporation of big data into clinical practice?
Reference:Mayo CS, Kessler ML, Eisbruch A, The Big Data Effort in Radiation Oncology: Data Mining or Data Farming? Advances in Radiation Oncology (2016) 1, 260-271. McNutt TR, Moore KL, Quon H. Needs and Challenges for Big Data in Radiation Oncology. Int J Radiat Oncol Biol Phys. 2016 Jul 1;95(3):909-15.
Choice A:Use of standards.
Choice B:Database and analytics technologies.
Choice C:Modifying clinical process to improve availability and curation.
Choice D:Protecting patient health information.
Choice E:All of the above.
Question 5: A key component needed for enablement of big data utilization in radiation therapy is:
Reference:Potters L, Ford E, Evans S, Pawlicki T, Mutic S. A Systems Approach Using Big Data to Improve Safety and Quality in Radiation Oncology. Int J Radiat Oncol Biol Phys. 2016 Jul 1;95(3):885-9. doi: 10.1016/j.ijrobp.2015.10.024. Epub 2015 Oct 21.
Choice A:Larger number of paperless facilities.
Choice B:Greater automation of delivery.
Choice C:An AAPM task group to address this issue.
Choice D:Unified ontology and corresponding standardization.
Choice E:Narrowing of data elements included in the big data collection.
Question 6: Potentially important source of big data in radiation therapy are: 
Reference:Potters L, Ford E, Evans S, Pawlicki T, Mutic S. A Systems Approach Using Big Data to Improve Safety and Quality in Radiation Oncology. Int J Radiat Oncol Biol Phys. 2016 Jul 1;95(3):885-9. doi: 10.1016/j.ijrobp.2015.10.024. Epub 2015 Oct 21.
Choice A:Formation of treatment plan archives at individual clinics.
Choice B:Insurance claims data.
Choice C:RO-ILS.
Choice D:B and C.
Choice E:All of the above.
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