| Question 1: Which of the following factors has a large impact on dosimetric uncertainty related to normal tissue OAR variations? |
| Reference: | Cui Y, Chen W, Kong FM, Olsen L, Beatty RE, Maxim PG, Ritter T, Sohn JW, Higgins J, Galvin JM, Xiao Y, ‘Contouring variations and the role of atlas in non-small cell lung cancer radiation therapy: Analysis of a multi-institutional preclinical trial planning study’, Practical Radiation Oncology April 2015 5(2), e67-e75,http://doi.org/10.1016/j.prro.2014.05.005. |
| Choice A: | Proximity to the target. |
| Choice B: | Maximum dose as an evaluation criteria. |
| Choice C: | Mean dose objective an evaluation criteria. |
| Choice D: | Volume of the normal tissue OAR. |
| Question 2: Inter-observer variability (IOV) in target volume and organ-at-risk (OAR) delineation has been shown to be significantly reduced by incorporating contouring guidelines, autocontouring, and multi-modality pre-imaging studies into the planning process. |
| Reference: | SK Vinod MBBS, MD, FRANZCR; M Min MBBS, FRANZCR; MG Jameson B Med Rad Phys; LC Holloway PhD., ‘A review of interventsion to reduce inter-observer variability in volume delineation in radiation oncology’, Journal of Medical imaging and Radiation Oncology June 2016, 60 (3), 393 – 406, DOI: 10.1111/1754-9485.1246
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| Choice A: | True. |
| Choice B: | False. |
| Question 3: What is a practical limitation of assessing contour quality? |
| Reference: | Beasley WJ, McWilliam A, Slevin NJ, Mackay RI, van Herk M. An automated workflow for patient-specific quality control of contour propagation. Phys Med Biol 2016;61:8577–86. |
| Choice A: | There are often insufficient example cases with errors used for developing algorithms. |
| Choice B: | Inter- and intra-observer variation result in uncertainty in the ground truth contours. |
| Choice C: | Machine learning methods are ill-suited to use the required input data for identifying errors. |
| Choice D: | Supervised learning will also be required which is too resource intensive on a large scale. |
| Choice E: | None of the above. |
| Question 4: A receiver operator characteristic (ROC) curve is an appropriate method to score detection of contour errors ? |
| Reference: | McIntosh C, Svistoun I, Purdie TG. Groupwise conditional random forests for automatic shape classification and contour quality assessment in radiotherapy planning. IEEE Trans Med Imaging 2013;32:1043–57. |
| Choice A: | True. |
| Choice B: | False. |
| Question 5: Transitioning from a single treatment plan to daily real-time adaptive replanning means that contouring errors move from systematic errors to random errors. |
| Reference: | van Herk M, Remeijer P, Rasch C, Lebesque JV, ‘The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy’, Int J Radiat Oncol Biol Phys. 2000 Jul 1;47(4):1121-35. |
| Choice A: | True. |
| Choice B: | False. |
| Question 6: Which image segmentation algorithm was shown to be nearly independent of local image contrast in an on-bard MR image guidance study? |
| Reference: | : Feng Y, Kawrakow I, Olsen J, Parikh PJ, Noel C, Wooten O, Du D, Mutic S, Hu Y, ‘A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT’, J Appl Clin Med Phys. 2016 Mar;17(2):441-460. doi: 10.1120/jacmp.v17i2.5820
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| Choice A: | Thresholding. |
| Choice B: | Fuzzy k-means. |
| Choice C: | K-harmonic means. |
| Choice D: | Reaction-diffusion level set evolution. |
| Choice E: | Tissue tracking algorithm provided by the ViewRay treatment planning and delivery system. |
