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SU-D-211-03: An Automated Inverse Planning Optimization Approach for Single- Fraction and Fractionated Radiosurgery

K Ghobadi^1,2,3, H Ghaffari^1,2,3, D Aleman^1,2,3

¹University of Toronto, Toronto, Ontario, Canada.

Medical Physics

|May 19, 2017

Summary

This summary is machine-generated.

This study developed an automated inverse planning method for Gamma Knife stereotactic radiosurgery (SRS). The approach improves dose conformity and organ-at-risk sparing for single-fraction SRS and generates homogeneous plans for fractionated SRS.

Keywords:

Cancer Optimization Radiosurgery

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Area of Science:

Medical Physics
Radiation Oncology
Computational Biology

Background:

Stereotactic radiosurgery (SRS) requires precise radiation delivery.
Manual treatment planning for SRS is time-consuming and complex.
Automated planning can potentially improve efficiency and plan quality.

Purpose of the Study:

To develop an automated inverse planning approach for Gamma Knife Perfexion.
To generate single-fraction and fractionated SRS treatment plans.
To optimize dose distribution for improved tumor coverage and organ-at-risk (OAR) sparing.

Main Methods:

A two-step automated approach: 1) grassfire-based isocenter localization, 2) penalty-based optimization for shot shapes and intensities.
Single-fraction SRS prioritized conformity to the gross tumor volume (GTV) with margin-less approach.
Fractionated SRS prioritized dose homogeneity, using planning target volumes (PTVs) and accounting for OAR overlap.

Main Results:

Automated planning achieved improved conformity and OAR sparing for single-fraction SRS.
Fractionated SRS plans demonstrated good PTV coverage (V95=94.9%) and acceptable OAR doses.
A tradeoff between conformity and OAR sparing was observed, with increased sensitivity to isocenter locations in fractionated plans.

Conclusions:

Automated inverse planning is effective for single-fraction SRS, enhancing conformity and OAR sparing.
The method can generate homogeneous plans for fractionated SRS.
This automated approach shows promise for optimizing SRS treatment planning.