| Question 1: Why are doctors bad at predicting outcomes? |
| Reference: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555846/
Predicting outcomes in radiation oncology —multifactorial decision support systems
Philippe Lambin, Ruud G. P. M. van Stiphout, Maud H. W. Starmans, et al
Nat Rev Clin Oncol. 2013 Jan; 10(1): 27–40.
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| Choice A: | They get confused by too much data. |
| Choice B: | They have too many options to choose from. |
| Choice C: | There is not enough good evidence to make a choice. |
| Choice D: | All of the above. |
| Question 2: What is the best way to validate a model? |
| Reference: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555846/
Predicting outcomes in radiation oncology —multifactorial decision support systems
Philippe Lambin, Ruud G. P. M. van Stiphout, Maud H. W. Starmans, et al
Nat Rev Clin Oncol. 2013 Jan; 10(1): 27–40.
|
| Choice A: | Bootstrap your training set to create a validation set. |
| Choice B: | Send the model to another hospital and have them validate it. |
| Choice C: | Get data from another hospital to validate. |
| Choice D: | Split your own data before modeling into a training and validation set. |
| Question 3: What is generally the best way to improve models in machine learning? |
| Reference: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555846/
Predicting outcomes in radiation oncology —multifactorial decision support systems
Philippe Lambin, Ruud G. P. M. van Stiphout, Maud H. W. Starmans, et al
Nat Rev Clin Oncol. 2013 Jan; 10(1): 27–40.
|
| Choice A: | Remove outlying patients from the training dataset. |
| Choice B: | Extend the training dataset with more patients. |
| Choice C: | Extend the training dataset with more features. |
| Choice D: | Use a better machine learning algorithm. |
| Question 4: The role of prior knowledge in autonomous and knowledge based treatment planning are, EXCEPT: |
| Reference: | Xing L, Using Population-Based Prior Knowledge to Autopilot Radiation Therapy Treatment Planning, Medical Physics 44, 389-96, 2017. |
| Choice A: | Provides guidance and benchmarks during the solution search for a case under planning. |
| Choice B: | Allows treatment plans for various disease sites to be compared to many other similar plans. |
| Choice C: | Can include adverse effects of radiation on normal tissues. |
| Choice D: | Can include adverse effects of radiation on normal tissues. |
| Question 5: Radiomics is an emerging field of research and promises to advance significantly patient care. All of the following are true, EXCEPT |
| Reference: | Cui Y, Tha KK, Terasaka S, Yamaguchi S, Wang J, Kudo K, Xing L, Shirato H, Li R. Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images. Radiology. 2016 Feb;278(2):546-53. |
| Choice A: | It uses a large amount of imaging data for prognostic prediction of outcome of the patients. |
| Choice B: | It quantifies tumor phenotypic characteristics by applying feature algorithms to imaging data. |
| Choice C: | It is used in current knowledge based treatment planning. |
| Choice D: | It represents a significant component of big data initiatives aimed at drawing inferences from large data sets that are not derived from carefully controlled experiments. |
| Question 6: Which answer is not a major challenge for the implementation of Learning Health Systems for Radiation Oncology? |
| Reference: | McNutt T., Moore K., Quon H. “Needs and Challenges for Big Data in Radiation Oncology,” Int’l J. of Radiation Oncology, Biology, Physics. Published online: November 27 2015. |
| Choice A: | Clinical culture and documentation to support the quantification of the patient condition. |
| Choice B: | Database technology to enable large scale access to complex RT data. |
| Choice C: | Data science models that can predict outcomes and are consistent with existing knowledge. |
| Choice D: | Data completeness and integrity. |
