| Question 1: The risk factors that may increase a person’s chances of developing cancer include:
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| 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.
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| 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.
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| 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
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| 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.
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| 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.
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| 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.
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| 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. |
