Adapting to the motion of multiple independent targets using multileaf collimator tracking for locally advanced prostate cancer: Proof of principle simulation study

Abstract

Purpose For patients with locally advanced cancer, multiple targets are treated simultaneously with radiotherapy. Differential motion between targets can compromise the treatment accuracy, yet there are currently no methods able to adapt to independent target motion. This study developed a multileaf collimator (MLC) tracking algorithm for differential motion adaptation and evaluated it in simulated treatments of locally advanced prostate cancer. Methods A multi‐target MLC tracking algorithm was developed that consisted of three steps: (a) dividing the MLC aperture into two possibly overlapping sections assigned to the prostate and lymph nodes, (b) calculating the ideally shaped MLC aperture as a union of the individually translated sections, and (c) fitting the MLC positions to the ideal aperture shape within the physical constraints of the MLC leaves. The multi‐target tracking method was evaluated and compared with two existing motion management methods: single‐target tracking and no tracking. Treatment simulations of six locally advanced prostate cancer patients with three prostate motion traces were performed for all three motion adaptation methods. The geometric error for each motion adaptation method was calculated using the area of overexposure and underexposure of each field. The dosimetric error was estimated by calculating the dose delivered to the prostate, lymph nodes, bladder, rectum, and small bowel using a motion‐encoded dose reconstruction method. Results Multi‐target MLC tracking showed an average improvement in geometric error of 84% compared to single‐target tracking, and 83% compared to no tracking. Multi‐target tracking maintained dose coverage to the prostate clinical target volume (CTV) D98% and planning target volume (PTV) D95% to within 4.8% and 3.9% of the planned values, compared to 1.4% and 0.7% with single‐target tracking, and 20.4% and 31.8% with no tracking. With multi‐target tracking, the node CTV D95%, PTV D90%, and gross tumor volume (GTV) D95% were within 0.3%, 0.6%, and 0.3% of the planned values, compared to 9.1%, 11.2%, and 21.1% for single‐target tracking, and 0.8%, 2.0%, and 3.2% with no tracking. The small bowel V57% was maintained within 0.2% to the plan using multi‐target tracking, compared to 8% and 3.5% for single‐target tracking and no tracking, respectively. Meanwhile, the bladder and rectum V50% increased by up to 13.6% and 5.2%, respectively, using multi‐target tracking, compared to 2.7% and 1.9% for single‐target tracking, and 11.2% and 11.5% for no tracking. Conclusions A multi‐target tracking algorithm was developed and tracked the prostate and lymph nodes independently during simulated treatments. As the algorithm optimizes for target coverage, tracking both targets simultaneously may increase the dose delivered to the organs at risk.

Optimising multi-target multileaf collimator tracking using real-time dose for locally advanced prostate cancer patients

“…To evaluate the performance of dose-optimised multi-target MLC tracking, the algorithm was tested in silico and compared to the geometric-optimised multi-target MLC tracking method which has been described in further detail by Hewson et al (2021a), as well as standard of care treatment where motion for each target is not managed during treatment.…”

“…However, currently available commercial systems are not able to adapt to multiple targets that have independent motion. Multileaf collimator (MLC) tracking (Sawant et al 2008, Falk et al 2010 can be implemented on standard clinical systems and has been utilised to develop a multi-target tracking method that was demonstrated to track multiple independent targets in a simulation study (Hewson et al 2021a), as well as experimentally on a standard linac (Hewson et al 2021b) and an MRI-linac (Liu et al 2020).…”

“…Geometric-optimised MLC tracking does not account for the errors that accumulate throughout a treatment, instead only optimising for the 2D geometry at each timestep throughout the treatment. In addition to these limitations, multi-target MLC tracking was also found to result in dosimetric error due to limitations with forming an adapted aperture for independently moving targets that overlap in the BEV (Hewson et al 2021a(Hewson et al , 2021b.…”