A method to calculate coverage probability from uncertainties in radiotherapy via a statistical shape model

Area of Science:

• Medical Physics
• Radiotherapy
• Image Analysis

Background:

• Geometric uncertainties in radiotherapy can significantly impact treatment accuracy.
• Modeling these uncertainties is crucial for optimizing treatment margins and patient safety.
• Existing methods may not fully capture complex variations like organ motion and observer variability.

Purpose of the Study:

• To develop and validate a novel technique for modeling geometric uncertainties in radiotherapy.
• To simultaneously analyze observer delineation variability, organ motion, and patient setup errors.
• To generate a coverage probability matrix for improved treatment margin determination and dose uncertainty calculation.

Main Methods:

• Creation of a point distribution model (PDM) from in-treatment cone beam CT scans.
• Simultaneous analysis of non-uniform observer delineation variability and organ motion.
• Statistical shape modeling without assuming rigid body motion.
• Generation of a coverage probability matrix from the PDM.

Main Results:

• Demonstrated the technique using rectal delineations from a prostate cancer patient.
• Constructed two PDMs: one including setup errors and one without.
• Confirmed Gaussian distribution of PDM variability, consistent with literature.
• Showcased differences in coverage probability matrices due to the inclusion of setup errors.

Conclusions:

• The developed technique effectively models geometric uncertainties in radiotherapy.
• The novel method provides a statistically meaningful way to assess shape variability.
• The coverage probability matrix aids in determining accurate treatment margins and dose uncertainties.