| Question 1: External breathing metrics have been demonstrated useful in ... |
| Reference: | Acquiring a four-dimensional computed tomography dataset using an external respiratory signal, Vedam, et al. Phys. Med. Biol. 48, 2003
A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing, Low et al, Med. 30, 2003 |
| Choice A: | Predicting the tumor motion |
| Choice B: | Reducing respiratory motion uncertainties |
| Choice C: | Sparing organs at risk |
| Choice D: | All of the above |
| Question 2: What can affect spirometry ? |
| Reference: | Comparison of spirometry and abdominal height as four-dimensional computed tomography metrics in lung, Lu et al, Med. Phys. 32 (7), 2005 |
| Choice A: | Patient positioning |
| Choice B: | Sensor placement |
| Choice C: | Signal drift |
| Choice D: | All of the above |
| Question 3: In GRID therapy, the valley to peak ratio refers to |
| Reference: | Spatially fractionated (GRID) radiation therapy using proton pencil beam scanning (PBS): Feasibility study and clinical implementation, Gao M., et al. Med Phys. 45(4), 2018 |
| Choice A: | the ratio of the low to high dose regions in a profile under the GRID |
| Choice B: | the ratio of the dose at tumor depth to the D(max) dose in a PDD under the. GRID |
| Choice C: | the ratio that cannot be defined when using a proton beam |
| Choice D: | the ratio of the proton plateau dose to the dose at the Bragg peak |
| Question 4: What is an advantage in using protons of GRID therapy, over photon based GRID therapy |
| Reference: | Spatially fractionated (GRID) radiation therapy using proton pencil beam scanning (PBS): Feasibility study and clinical implementation. Gao M., et al. Med Phys. 45(4), 2018. |
| Choice A: | Protons can be used to treat the target and stop before a critical structure |
| Choice B: | Proton GRID patterns can be optimized to deliver a uniform dose with depth |
| Choice C: | PBS proton methods can be used to provide 3-D conformity to odd shaped targets |
| Choice D: | All of the above |
| Question 5: A special consideration when planning proton GRID treatments should include: |
| Reference: | Spatially fractionated (GRID) radiation therapy using proton pencil beam scanning (PBS): Feasibility study and clinical implementation, Gao M., et al. Med Phys. 45(4), 2018. |
| Choice A: | Beam line proton every spread |
| Choice B: | An evaluation of proton range uncertainty |
| Choice C: | Ensuring appropriate PTV expansions |
| Choice D: | Patient blood oxygen levels |
| Question 6: What is the goal of LET-guided plan evaluation in IMPT? |
| Reference: | Mohan R, Peeler CR, Guan F, Bronk L, Cao W, Grosshans DR. Radiobiological issues in proton therapy. Acta Oncol. 2017;56(11):1367, 1373. |
| Choice A: | To make sure that the calculated dose is correct |
| Choice B: | To make sure that the calculated dose distribution does not violate the institutional dose volume constraints |
| Choice C: | To make sure that there is no overlap of high dose and high LET in nearby critical organs |
| Choice D: | To make sure that the calculated LET is correct |
| Question 7: What is the goal of the LET-guided robust optimization in IMPT? |
| Reference: | Y. An, J. Shan, S.H. Patel, W. Wong, S.E. Schild, X. Ding, M. Bues, W. Liu, Robust intensity-modulated proton therapy to reduce high linear energy transfer in organs at risk, Medical physics 44, 6138-6147 2017. |
| Choice A: | To have higher LET in normal tissues |
| Choice B: | To have lower LET in tumors |
| Choice C: | To improve LET distribution while sacrificing the physical dose distribution quality and plan robustness |
| Choice D: | To redistribute high LET from critical organs to tumors without sacrificing the physics dose distribution quality and plan robustness |
| Question 8: Knowledge-based models, built from a large enough samples of patient data, may be valuable in the following ways: |
| Reference: | Using a knowledge-based planning solution to select patients for proton therapy. Radiother Oncol. 2017 Aug;124(2):263-270 |
| Choice A: | Clinical decision support |
| Choice B: | Predicting dosimetric advantage of a given modality |
| Choice C: | Improved efficiency |
| Choice D: | Improved standardization |
| Choice E: | Quality control |
| Choice F: | All of the above |
| Question 9: One study found that accurate classification of a knowledge-based dosimetric model for oropharynx head and neck cancer requires how many datasets? |
| Reference: | Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol. 2017 Dec;125(3):392-397. |
| Choice A: | 5 or less |
| Choice B: | 10-15 |
| Choice C: | At least 30 |
| Question 10: Particularly for proton therapy, when applying a model to a specific patient treatment plan, beam arrangement has a significant impact. |
| Reference: | Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study, Cancers (Basel). 2018 Nov; 10(11): 420. |
| Choice A: | True |
| Choice B: | False |
