2020 Joint AAPM | COMP Virtual Meeting
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Session Title: Radiation Therapy in the Era of Artificial Intelligence
Question 1: Which of these is NOT a neural network architecture?
Reference:Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Choice A:Convolutional Neural Network
Choice B:Recurrent Neural Network
Choice C:Fully Connected Neural Network
Choice D:Host Access Neural Network
Question 2: What are the optimal images for radiation therapy treatment planning? Use CT simulation images as an example:
Reference:Barrett, H. H., Kupinski, M. A., Muleller, S., Halpern, H. J., Morris III, J. C., and Dwyer, R., “Objective assessment of image quality vi: Imaging in radiation therapy," Physics in medicine and biology 58(22), 8197 (2013).
Choice A:Images acquired with minimal dose.
Choice B:Images acquired with as maximum as possible radiation dose allowed by an CT scanner.
Choice C:Images acquired with the scanning parameters optimized based on the performance of target contouring methods.
Choice D:Images acquired with the scanning parameters optimized based on the treatment outcome.
Choice E:Does not matter, CBCT image quality is more important
Question 3: Which applications can the therapeutic operating characteristic (TOC) curve be employed for?
Reference:Barrett, H. H., Myers, K. J., Hoeschen, C., Kupinski, M. A., and Little, M. P., “Task-based measures of image quality and their relation to radiation dose and patient risk," Physics in Medicine & Biology 60(2), R1 (2015).
Choice A:Comparison of different imaging instruments
Choice B:Comparison of OAR and target delineation, and image registration algorithms
Choice C:Comparison of RT treatment planning algorithms
Choice D:Determination of optimal treatment dose for individual patients when biological accuracy can be clearly established
Choice E:All of the above
Question 4: Which of these is a potential application of artificial intelligence in treatment planning?
Reference:Shen, C., Nguyen, D., Zhou, Z., Jiang, S. B., Dong, B., & Jia, X. (2020). An introduction to deep learning in medical physics: advantages, potential, and challenges. Physics in Medicine & Biology, 65(5), 05TR01.
Choice A:Dose/DVH Prediction
Choice B:Pareto surface navigation
Choice C:DRL for parameter tuning
Choice D:Outcome based planning
Choice E:All of the above
Question 5: If the validation of a prediction model shows worse model performance, it means:
Reference:van Soest J, Meldolesi E, van Stiphout R, Gatta R, Damiani A, Valentini V, et al. Prospective validation of pathologic complete response models in rectal cancer: Transferability and reproducibility. Med Phys 2017;44:4961–7. https://doi.org/10.1002/mp.12423.
Choice A:The model was trained on a biased dataset
Choice B:The validation dataset is biased
Choice C:We need to know the cohort differences before we can draw any conclusion
Choice D:The model needs to be re-learned on the validation dataset
Question 6: Can toxicity models be re-purposed for different primary tumor locations?
Reference:Shi Z, Foley KG, Pablo de Mey J, Spezi E, Whybra P, Crosby T, et al. External Validation of Radiation-Induced Dyspnea Models on Esophageal Cancer Radiotherapy Patients. Front Oncol 2019;9. https://doi.org/10.3389/fonc.2019.01411.
Choice A:No, primary tumor locations are leading
Choice B:Yes, if the model is properly tested and the AI input is unrelated to the primary tumor
Choice C:No, re-purposing AI models is never a good option
Choice D:Yes, if the model does work, no adaptation is needed
Question 7: Which of the following is a valid method for error detection in radiation therapy:
Reference:McNutt, T. R., et al. Use of Big Data for Quality Assurance in Radiation Therapy. Seminars in Radiation Oncology 29(4): 326 – 332 (2019).
Choice A:Classification Model
Choice B:Statistical Outlier
Choice C:Random Forest
Choice D:Convolutional Neural Networks (CNNs)
Choice E:All of the above
Question 8: The ideal method to evaluate a model for error detection that will be deployed clinically is:
Reference:Kalet, A.M., et al. Quality assurance tasks and tools: the many roles of machine learning. Med Phys 47(5): e168-e177 (2019).
Choice A:Leave-one-out validation from a single-institution dataset
Choice B:Two sets of data from multiple institutions: learning and testing
Choice C:Three sets of data from multiple institutions: learning, testing, and validation
Choice D:Three sets of data from one institution: learning, testing, and validation
Question 9: To establish a causal relationship, we need to assume that:
Reference:“Targeted Learning: Causal Inference for Observational and Experimental Data.” Mark Van der Laan and Sherri Rose. Springer. 2011.
Choice A:There are no hidden confounders and a big enough dataset.
Choice B:The covariates are orthonormal to each other.
Choice C:The model is interpretable.
Choice D:The algorithm is a consistent estimator.
Question 10: The incorporation of Expert Knowledge into ML algorithms can:
Reference:Gennatas, Efstathios D., et al. "Expert-augmented machine learning." Proceedings of the National Academy of Sciences 117.9 (2020): 4571-4577.
Choice A:Allow to train with less data.
Choice B:Help discover hidden confounders.
Choice C:Make models more robust to covariate shifts.
Choice D:All of the above.
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