(Monday, 4/6/2020) 4:30 PM - 5:30 PM [Mountain Time (GMT-6)]
Understanding Radiobiological Outcomes Using Analytics from a Prospective Stereotactic Radiosurgery Registry
Methods to better visualize and analyze the increased data available in radiation oncology would be extremely useful for the medical physics community. With Data Science being an important initiative of the AAPM, guidance should be given on the preferred statistic test for types of studies. Efforts should be made in moving analysis from Excel to more powerful tools such as R. Learning how to manipulate dataframes as opposed to spreadsheets and to create visualizations that are easily communicated to the general public (patients and non radiation oncology physicians).
Learning Objectives:
1. Motivation for clinical physicists to delve into outcomes analytics.
2. Creating outcome analytics for radiosurgery.
3. Using retrospective randomization with bootstrap sampling in outcome analytics.
4. Review statistical methods in creating Kaplan-Meier survival curves with Cox Regression.
5. Show the effect of early censoring on statistical power and effects of outliers on testing.
IMRT QA evaluation
Patient-specific IMRT QA evaluation is an important clinical process to ensure safe treatments for medical physicist. In the past years, parameters such as dose difference, DTA, gamma index have been used for the plan evaluation. However, those parameters are limited by certain aspects. For example, the parameters do not consider the PTV or OAR difference, do not consider structure shape, do not consider measurement equipment differences. New evaluation methods should be used for the near era treatment, such as 3D DVH, SSIM, gamma+ etc. In this education SAM, we would like to introduce those new parameters and show the pros/cons.
Learning Objectives:
1. Introduce new IMRT QA evaluation parameters
2. Understand the new parameters pros/cons
3. Understand practical considerations in implementing those parameters
Handouts
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