ArticleMedical physics2026
Towards automated dose-guided patient positioning at clinical timescale in head-and-neck cancer proton radiotherapy.
Article in Medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIn intensity modulated proton therapy (IMPT) accurate patient positioning prior to treatment delivery is important to ensure that the prescribed dose is delivered as intended. Typically, in-room 3D imaging is utilized to position the patient based on anatomical features, which can be challenging due to interfractional anatomical changes. PURPOSE: This study aims to introduce a fast automated in-room dose-guided patient positioning (DGPP) algorithm and to evaluate its performance for obtaining accurate target coverage in head-and-neck cancer (HNC) patients.
methodsThe DGPP algorithm employs Moqui, an open-source Monte Carlo (MC) dose engine, for fast proton dose computations. A beam model was configured specific to our institute's treatment machine using measured beam data and it was compared to the clinical treatment planning system (TPS) by means of a 2%/2 mm gamma analysis of 45 HNC treatment plan dose computations. The same cohort of 45 HNC patients, each with five to seven weekly repeat CTs (rCTs) acquired for longitudinal treatment quality assurance, was used to evaluate DGPP performance. For each rCT, the patient position relative to the plan isocenter was optimized using gradient descent to maximize the
resultsThe 3D 2%/2 mm gamma pass rate of Moqui dose computations, with as reference the doses computed in the clinical TPS, was at least 99.1%. By employing DGPP to optimize the rCT positions we found
conclusionsWe demonstrated that with a gradient descent algorithm for DGPP using a GPU MC proton dose engine, accurate automated HNC patient alignment to the CTV is possible at near-clinically acceptable timescale for pre-treatment patient positioning. Further work is warranted to facilitate clinical adoption.
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