| Question 1: Conceptually, what is the difference between using human-engineered radiomic features and deep learning in characterizing tumors for assessing treatment response? |
| Reference: | 1. Giger ML, Karssemeijer N, Schnabel J: Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annual Review of Biomedical Engineering15:327-357, 2013.
2. Giger ML: Machine Learning in Medical Imaging. J Am Coll Radiol. 2018 Mar;15 (3 Pt B):512-520. doi: 10.1016/j.jacr.2017.12.028. Epub Feb 2, 2018.
3. Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML: Deep learning in medical imaging and radiation therapy. Medical Physics, 2018 |
| Choice A: | Intuitive understanding |
| Choice B: | b. Benefit to the progonsis |
| Choice C: | Calculation time once system is trained |
| Choice D: | Need for medical truth for the evaluation. |
| Question 2: What may be characteristics of therapeutic biomarkers? |
| Reference: | 1. Giger ML, Karssemeijer N, Schnabel J: Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annual Review of Biomedical Engineering15:327-357, 2013.
2. Giger ML: Machine Learning in Medical Imaging. J Am Coll Radiol. 2018 Mar;15 (3 Pt B):512-520. doi: 10.1016/j.jacr.2017.12.028. Epub Feb 2, 2018.
3. Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML: Deep learning in medical imaging and radiation therapy. Medical Physics, 2018 |
| Choice A: | Single feature characteristic |
| Choice B: | Merged characteristic via a tumor signature |
| Choice C: | Change in biomarker over treatment |
| Choice D: | Correlation with cancer subtypes |
| Choice E: | All of the above. |
| Question 3: Habitat imaging aims to investigate how the subclones within a tumor manifests at radiologic scan level. |
| Reference: | Gillies, Robert J., and Yoganand Balagurunathan. "Perfusion MR Imaging of Breast Cancer: Insights Using “Habitat Imagingâ€." Radiology 288 (2018): 36-37. |
| Choice A: | True. |
| Choice B: | False. |
| Question 4: Habitat regions learned with unsupervised clustering can potentially detect intrinsic intratumor heterogeneity with relevant clinical values |
| Reference: | Wu, Jia, et al. "Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy." Radiology 288.1 (2018): 26-35. |
| Choice A: | True |
| Choice B: | False |
| Question 5: Aggressive breast cancer tumors were shown to have a larger volume of |
| Reference: | Wu, Jia, et al. "Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy." Radiology 288.1 (2018): 26-35.
(Question added by Kristy Brock) |
| Choice A: | The poorly perfused subregion |
| Choice B: | The highly perfused subregion |
| Choice C: | Randomly perfused subregion |
| Choice D: | Absolutely no perfusion |
| Choice E: | Complete blood pooling |
| Question 6: Updates on serum carbohydrate antigen demonstrate that the combined analysis of studies reporting CEA, the sensitivity and specificity were both greater than 95%. |
| Reference: | Poruk KE, Gay DZ, Brown K, et al. The clinical utility of CA 19-9 in pancreatic adenocarcinoma: diagnostic and prognostic updates. Curr Mol Med. 2013;13(3):340–351. (Question added by Kristy Brock) |
| Choice A: | True |
| Choice B: | False |
