| Question 1: Which of following is not a radiomics feature?
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| Reference: | Sarah A. Mattonen et al, Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment. Int J Radiation Oncol Biol Phys, Vol. 94, No. 5, pp. 1121-1128, 2016 http://dx.doi.org/10.1016/j.ijrobp.2015.12.369 |
| Choice A: | Grey-level co-occurrence matrix correlation. |
| Choice B: | Grey-level co-occurrence matrix energy. |
| Choice C: | Grey-level co-occurrence matrix homogeneity. |
| Choice D: | Morphology. |
| Choice E: | Mean Dose. |
| Question 2: Which of the following clinical applications can radiomics potentially lead to improvements in:
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| Reference: | Aerts, H.J., The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncology, 2016. 2(12):1636-1642.
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| Choice A: | Diagnosis. |
| Choice B: | Prediction of prognosis. |
| Choice C: | Assessment of treatment response. |
| Choice D: | All of the above. |
| Choice E: | None of the above. |
| Question 3: Which of the following are NOT the advantages of imaging compared with tissue biopsy-based pathologic or molecular analysis:
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| Reference: | Gillies, R.J., Kinahan, P.E., and Hricak, H., Radiomics: images are more than pictures, they are data. Radiology, 2016. 278(2):563-577.
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| Choice A: | Noninvasive. |
| Choice B: | Provides definitive diagnosis of cancer. |
| Choice C: | Allows visualization of the whole tumor. |
| Choice D: | Allows characterization of in vivo pathophysiology. |
| Choice E: | None of the above. |
| Question 4: A radiogenomic association study may be designed to answer the following scientific questions EXCEPT:
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| Reference: | Kuo, Michael D., and Neema Jamshidi. "Behind the numbers: decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations." Radiology 270.2 (2014): 320-325 |
| Choice A: | Understanding the genomic features correlated with certain image phenotypes. |
| Choice B: | Understanding how a certain biological process is reflected at imaging. |
| Choice C: | Defining imaging surrogates of tissue-based molecular markers. |
| Choice D: | Defining causal relations between imaging and molecular markers. |
| Question 5: Which of the following is not characteristic of an ideal quantitative imaging metric?
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| Reference: | Characterization of PET/CT images using texture analysis.: the past, the present . . . any future? Mathiew Hatt, et al., Eur. J. Nucl. Med. Mol. Imaging, 44:151 – 165, (2016)
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| Choice A: | Robust. |
| Choice B: | Repeatable. |
| Choice C: | Correlated with clinical prognostic factors. |
| Choice D: | Correlated with clinical end-point. |
| Question 6: Which of the following statistics is appropriate for measuring the volume correlation of a quantitative imaging feature?
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| Reference: | Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer, Xenia Fave, et al., Scientific Reports, 7:588, (2017); Characterization of PET/CT images using texture analysis.: the past, the present . . . any future? Mathiew Hatt, et al., Eur. J. Nucl. Med. Mol. Imaging, 44:151 – 165, (2016)
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| Choice A: | Concordance correlation coefficient. |
| Choice B: | Overall concordance correlation coefficient. |
| Choice C: | Pearson correlation coefficient. |
| Choice D: | Spearman correlation coefficient. |
