| Question 1: Data-driven Treatment Plan Quality Control |
| Reference: | Experience-Based Quality Control of Clinical IMRT Planning
Moore, Kevin L.; Brame, R. Scott; Low, Daniel A.; Mutic, S.; INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY * BIOLOGY * PHYSICS Volume: 81 Issue: 2 Pages: 545-551 |
| Choice A: | Eliminates plans that will fail IMRT QA at the treatment machine |
| Choice B: | Highlights dose calculation errors due to inhomogeneities |
| Choice C: | Guarantees that patients will not receive dose to critical structures that exceeds tolerance levels |
| Choice D: | Eliminates prescription dose from PTV-OAR overlap regions |
| Choice E: | Can flag clinically significant excess dose to critical structures |
| Question 2: Treatment Plan Quality |
| Reference: | Predicting dose-volume histograms for organs-at-risk in IMRT planning, Appenzoller, Lindsey M.; Michalski, Jeff M.; Thorstad, Wade L.; et al. MEDICAL PHYSICS Volume: 39 Issue: 12 Pages: 7446-7461 |
| Choice A: | Cannot be predicted using previously treated patient plans |
| Choice B: | Cannot be improved by retrospective and objective plan review |
| Choice C: | Metrics can be developed using previous plans to alert the user that their current plan is suboptimal |
| Choice D: | Is already standardized throughout the industry and needs no improvement |
| Choice E: | Is always guaranteed when using modern treatment planning systems |
| Question 3: The minimum number of cases to train an automated planning model can readily be determined based on training validation results? |
| Reference: | McIntosh, C., & Purdie, T. G. (2016). Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy. IEEE Transactions on Medical Imaging, 35(4), 1000–1012. https://doi.org/10.1109/TMI.2015.2505188 |
| Choice A: | True |
| Choice B: | False |
| Question 4: Which of the following is not a common metrics that be used to validate automated planning results? |
| Reference: | McIntosh, C., & Purdie, T. G. (2016). Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy. IEEE Transactions on Medical Imaging, 35(4), 1000–1012. https://doi.org/10.1109/TMI.2015.2505188 |
| Choice A: | DICE metric |
| Choice B: | areas under the curve from receiver operator curve (ROC) |
| Choice C: | gamma metric |
| Choice D: | mean absolute difference of DVH |
| Question 5: The purpose of using KBP in multi-institutional trials is to attempt to limit the number of trial participants treated with suboptimal treatment plans: |
| Reference: | Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126; Kevin Moore et al; Int J of Radiation Oncol Biol Phys, Vol 92, No. 2,pp. 228-235, 2015 |
| Choice A: | True |
| Choice B: | False |
| Question 6: Even though the planning target volume (PTV) meets the protocol guidelines, it is possible to utilize the KBP to improve the sparing of organs-at-risk (OAR): |
| Reference: | Tol JP, et al., Analysis of EORTC-1219-DAHANCA-29 trial plans demonstrates the potential of knowledge-based planning to provide patient-specific treatment plan quality assurance. Radiother Oncol 130, pp75-81, 2019. |
| Choice A: | True |
| Choice B: | False |
