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SU-E-T-214: Predicting Plan Quality from Patient Geometry: Feature Selection and Inference Modeling.

D Ruan¹, W Shao¹, J DeMarco¹

¹UCLA Department of Radiation Oncology, Los Angeles, CA.

Medical Physics

|May 19, 2017

Summary

This summary is machine-generated.

This study developed methods to predict radiation therapy plan quality using geometric features of planning target volumes (PTV) and organs at risk (OAR). Relative geometry features significantly improved prediction accuracy, offering valuable insights for treatment planning.

Keywords:

Inference methods Linear regression Radiation therapy

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Precision Measurements and Parametric Models of Vertebral Endplates

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Published on: September 17, 2019

Area of Science:

Radiation oncology
Medical physics
Computational biology

Background:

Accurate radiation therapy planning is crucial for treatment efficacy and patient safety.
Predicting treatment plan quality before completion can optimize resource allocation and identify suboptimal plans.
Geometric features of planning target volumes (PTV) and organs at risk (OAR) are known to influence plan quality.

Purpose of the Study:

To develop and evaluate methods for inferring radiation therapy plan quality using geometric features of PTV and OAR structures.
To identify geometric features with high prognostic value for predicting treatment plan quality.
To establish a baseline for detecting plan abnormalities and setting reference goals for planners.

Main Methods:

Explored absolute (volumes) and relative (distance/overlap) geometric features of PTV and OARs.
Developed and assessed linear regression (including sparsity-penalized) and nonparametric models to predict dose-volume histogram (DVH) endpoints.
Utilized cross-validation for parameter selection and performance evaluation, focusing on prostate SBRT as a pilot site.

Main Results:

Sparsity-regularized linear regression identified predominantly absolute geometric features as having linear prognostic utility.
Incorporating relative geometric features improved plan quality prediction by 15% across all DVH endpoints.
Nonparametric models showed a greater reliance on relative geometry, excelling in predicting PTV coverage and conformality, while linear models slightly better predicted OAR DVH points.

Conclusions:

Inference models provide an expected plan quality benchmark prior to treatment planning, aiding planners and abnormality detection.
Relative geometry features offer crucial spatial configuration information, complementing absolute geometry.
The integration of both absolute and relative geometric features enhances the understanding of achievable plan conformality and prognostic value.