Medical physics · 2022
Knowledge-based planning algorithm for lung SBRT with robust Bayesian stochastic frontier analysis and missing data management.
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Kroshko Angelika, Morin Olivier, Archambault Louis
Аннотация
PURPOSE: A knowledge-based planning technique is developed based on Bayesian stochastic frontier analysis. A novel missing data management is applied in order to handle missing organs-at-risk and work with a complete dataset.
METHODS: Geometric metrics are used to predict DVH metrics for lung SBRT with a retrospective database of 299 patients. In total, 16 DVH metrics were predicted for the main bronchus, heart, esophagus, spinal cord PRV, great vessels, and chest wall. The predictive model is tested on a test group of 50 patients.
RESULTS: Mean difference between the observed and predicted values ranges between 1.5 ± 1.9 Gy and 4.9 ± 5.3 Gy for the spinal cord PRV D0.35cc and the main bronchus D0.035cc, respectively.
CONCLUSIONS: The missing data model implanted in the predictive model is robust in the estimation of the parameters. Bayesian stochastic frontier analysis with missing data management can be used to predict DVH metrics for lung SBRT treatment planning.
METHODS: Geometric metrics are used to predict DVH metrics for lung SBRT with a retrospective database of 299 patients. In total, 16 DVH metrics were predicted for the main bronchus, heart, esophagus, spinal cord PRV, great vessels, and chest wall. The predictive model is tested on a test group of 50 patients.
RESULTS: Mean difference between the observed and predicted values ranges between 1.5 ± 1.9 Gy and 4.9 ± 5.3 Gy for the spinal cord PRV D0.35cc and the main bronchus D0.035cc, respectively.
CONCLUSIONS: The missing data model implanted in the predictive model is robust in the estimation of the parameters. Bayesian stochastic frontier analysis with missing data management can be used to predict DVH metrics for lung SBRT treatment planning.