BEST IN PHYSICS (MULTI-DISCIPLINARY): Real-Time Dose-Optimized Multi-Target MLC Tracking for Locally Advanced Prostate Cancer
E Hewson1*, D Nguyen1,2,3, J Booth3,4, P Keall1, L Mejnertsen1, (1) ACRF Image X Institute, University of Sydney School of Health Sciences, Sydney, NSW, AU, (2) School of Biomedical Engineering, University of Technology Sydney, NSW, AU, (3) Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, AU, (4) School of Physics, University of Sydney, Sydney, NSW, AU
Presentations
SU-F-TRACK 6-5 (Sunday, 7/25/2021) 4:30 PM - 5:30 PM [Eastern Time (GMT-4)]
Purpose: The accuracy of radiotherapy for patients with locally advanced cancer is compromised by independent motion of multiple tumors. Multi-target MLC tracking has been developed to adapt to multiple targets in real time. However, geometrically adapting the MLC aperture to target motion by directly translating the planned aperture results in sub-optimal delivered dose. It was hypothesized that optimizing multi-target MLC tracking based on accumulated dose would reduce dosimetric errors compared to geometric-based multi-target tracking for locally advanced prostate cancer.
Methods: A dose-optimized multi-target tracking algorithm that adapts the MLC aperture to correct for dosimetric error was developed for moving prostate and static lymph node targets. The dose-optimized algorithm accumulated the planned dose to the prostate and lymph node PTVs during treatment and shifted the prostate dose volume in the direction of motion. The adapted MLC apertures were calculated to minimize the difference between the planned and delivered doses accumulated up to that point. This algorithm was evaluated by simulating three locally advanced prostate cancer VMAT plans and three prostate motion traces with a relative interfraction displacement. The performance of dose-optimized multi-target tracking was compared to geometric-based multi-target tracking and no tracking using a 3D gamma comparison between delivered and planned doses with a 2%/2 mm pass criterion.
Results: The mean gamma-failure rate was lowest for dose-optimized tracking, with 11.2±3.7% for the prostate and 0.6±0.4% for the nodes. The gamma-failure rate for geometric-based tracking was 24.4±7.0% for the prostate and 0.8±0.8% for the nodes. When no tracking was used, the gamma-failure rate was 34.0±15.6% for the prostate and 21.7±1.8% for the nodes.
Conclusion: This study developed a dose-optimized multi-target tracking method that can correct for dosimetric deviations during treatment for locally advanced prostate cancer. Dose-optimized tracking reduced error compared to geometric-based tracking and no tracking for both the prostate and nodes.
Funding Support, Disclosures, and Conflict of Interest: The authors acknowledge funding provided from a Cancer Council NSW Project Grant (AP1165097). P.J. Keall is an inventor on US patents 7,469,035 and 8,971,489 that are related to MLC tracking. Patent 7,469,035 is unlicensed; patent 8,971,489 is exclusively licensed to Asto CT.
Handouts
Keywords
MLC, Simulation, Targeted Radiotherapy
Taxonomy
TH- External Beam- Photons: adaptive therapy
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