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Robustness assessment of a novel 4D optimization approach for lung cancer radiotherapy

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thesis
posted on 24.05.2021, 15:10 by Shahad M. Al-Ward
One of the main challenges to treatment of lung cancer with radiation therapy is the tumor motion due to respiration. Previously, a novel approach was developed to generate treatment plans which compensate for respiratory motion and its variations. The worst case method is based on combining two intensity maps from two 4D plans optimized on the two worst cases of motion variations. The worst case planning method was previously tested on simulated motion variations. The goal of this project was to further test the worst case approach on realistic patient motion variations and treatment planning data. Two approaches to combining worst case plans were investigated: the first method takes the average of the two intensity maps, and the second method takes the maximum intensity of the two intensity maps. The robustness of worst case plans was compared with ITV plans and nominal 4D plans on three different motion variation scenarios. Study 1 and 2 investigated the robustness of the worst case methods on amplitude variations and patient motion variations on simulated image data. Study 3 investigated the robustness of the worst case methods on patient motion variations using real patient image data. The average intensity worst case method was only robust to Study 3 motion variations. The maximum intensity worst case method, the margin based, and the nominal approaches were not robust to any of the motion variations. Further evaluation over a wide range of tumour sizes, motion amplitudes and variability is required to determine the clinical applicability of the worst case planning method.

History

Language

eng

Degree

Master of Science

Program

Biomedical Physics

Granting Institution

Ryerson University

LAC Thesis Type

Thesis

Thesis Advisor

Dr. Emily Heath