2022 AAPM 64th Annual Meeting
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Session Title: Machine Intelligence in Imaging for Treatment Response Assessment and Prediction: Implications for Adaptive Radiation Therapy
Question 1: Intra-treatment imaging during a course of radiation therapy treatment:
Reference:Reference: Mowery, Yvonne M., Irina Vergalasova, Christel N. Rushing, Kingshuk Roy Choudhury, Donna Niedzwiecki, Qiuwen Wu, David S. Yoo, Shiva Das, Terence Z. Wong, and David M. Brizel. “Early 18F-FDG-PET Response During Radiation Therapy for HPV-Related Oropharyngeal Cancer May Predict Disease Recurrence.” Int J Radiat Oncol Biol Phys 108, no. 4, 969–76, 2020
Choice A:provides little information for future treatment guidance
Choice B:has become the standard of care in patients receiving definitive radiation or chemoradiation therapy
Choice C:is not recommended for HPV-related oropharynx cancer patients
Choice D:can potentially detect treatment response such that radiation dose can be adjusted
Question 2: For treatment outcome prediction in head and neck cancer patients:
Reference:Reference: H. Morgan, K. Wang, M. Dohopolski, X. Liang, M. Folkert, D. Sher, and J. Wang, Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment CBCT features, Quantitative Imaging in Medicine and Surgery, vol. 11, pp. 4781-4796, 2021
Choice A:baseline pre-treatment imaging is adequate to obtain a clinically useful model
Choice B:combinational analysis on pre- and post-treatment imaging, as well as clinical characteristics often results in better model performance
Choice C:we would only need intra-treatment imaging
Choice D:established biomarkers such as p16 status are sufficient
Question 3: Radiomics represents a method for the qualitative description of medical images.
Reference:Reference: van Timmeren, Janita E., et al. "Radiomics in medical imaging—“how-to” guide and critical reflection." Insights into imaging 11.1 (2020): 1-16.
Choice A:True
Choice B:False
Question 4: Radiomics research should focus on implementation of standardized radiomics features and software, together with external validation of models.
Reference:Reference: Chetan, Madhurima R., and Fergus V. Gleeson. "Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives." European radiology 31.2 (2021): 1049-1058.
Choice A:True
Choice B:False
Question 5: GBM re-irradiation techniques include:
Reference:Reference: Maria Chiara Lo Greco, Roberto Milazzotto, Rocco Luca Emanuele Liardo, Grazia Acquaviva, Madalina La Rocca, Roberto Altieri, Francesco Certo, Giuseppe Maria Barbagallo, Antonio Basile, Pietro Valerio Foti, Stefano Palmucci, Stefano Pergolizzi, Antonio Pontoriero, Corrado Spatola, Relapsing High Grade Glioma from Peritumoral Zone: Critical Review of Radiotherapy Treatment Options, Brain Sci. 2022 Apr; 12(4): 416. Published online 2022 Mar 22. doi: 10.3390/brainsci12040416, PMCID: PMC9027370
Choice A:Stereotactic radiosurgery
Choice B:Hypo-fractionated stereotactic radiotherapy
Choice C:Conventionally fractionated radiotherapy
Choice D:Brachytherapy
Choice E:All of above
Question 6: Potential stem cell niches such as subventricular zone and subgranular zone, and their geometric relationship to the initial GBM tumor
Reference:Reference: Yi Lao, Dan Ruan, April Vassantachart, Zhaoyang Fan, Jason Ye, Eric Chang, Robert Chin, Tania Kaprealian, Gabriel Zada, Mark Shiroishi, Ke Sheng, Wensha Yang, Voxel wise Prediction of Recurrent High-Grade Glioma via Proximity Estimation–Coupled Multidimensional Support Vector Machine, Int J Radiat Oncol Biol Phys. 2022 Apr 1;112(5):1279-1287. PMID: 34963559 PMCID: PMC8923952
Choice A:are associated with GBM recurrence patterns
Choice B:are associated with GBM patient survival
Choice C:can be comprehensively quantified on volumetric MR images
Choice D:provides new targeting information for localized salvage radiotherapy
Choice E:All of above are true.
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