Adaptive radiation therapy for real-time deformations using dose-optimised multileaf collimator tracking: an in-silico proof of concept for multiple lung lesions

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Abstract
Objective. Anatomy continuously deforms during radiation therapy. Although real-time volumetric imaging approaches are emerging, there is a lack of adaptive strategies that account for intrafraction deformations. The purpose of this study was to develop a multileaf collimator (MLC) tracking method that adapts to deformations and evaluate the performance for lung cancer with multiple lesions. Approach. Dose-optimised deformable MLC tracking was developed using a fast dose calculation to accumulate dose at each timestep. The accumulated planned doses were deformed to represent the desired dose distribution for the deformed anatomy and the MLC leaf positions were optimised to minimise the difference between the delivered and deformed planned dose. Dose-optimised deformable MLC tracking was evaluated using four lung cancer cases generated using the 4D XCAT digital phantom. Stereotactic ablative radiotherapy treatment plans were created using a planning target volume (PTV) margin expansion of 5 mm on the gross tumour volumes (GTV). Treatments were simulated using three patient-measured motions for each phantom. The doses accumulated using the fast dose calculation model with MLC tracking were compared to an internal target volume (ITV)-based approach. Main results. The volume of the PTVs were reduced by an average of 34% using dose-optimised deformable MLC tracking compared to the ITV-based approach. The mean differences and standard deviations from the planned doses were −0.5% ± 0.6% for the GTV D 100% and −1.1% ± 0.6% for the PTV D 98% when dose-optimised deformable MLC tracking was used, and −5.2% ± 8.8% for the ITV D 100% and −13.8% ± 12.9% for the PTV D 98% when no tracking was used. Significance. The study demonstrated a proof of concept for dose-optimised deformable MLC tracking to reduce dosimetric errors for deforming anatomy. The proposed method could enable the safe reduction of treatment margins for multiple independently moving targets in the lung compared to the standard of care.
Funding Information
  • Medical Research Future Fund (NCRI000139)
  • National Health and Medical Research Council (1194004)
  • Cancer Institute NSW (2022/ECF1418)

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