Towards automated dose‐guided patient positioning at clinical timescale in head‐and‐neck cancer proton radiotherapy

作者：Femke Oosterhof, Gabriel Guterres Marmitt, A. M. Kamerbeek, Jeffrey Free, Gerolf Meulman, Zohreh Shahpouri, Pietro Pisciotta, Johannes A. Langendijk, Stefan Both · 发表于：Medical Physics · 年份：2026 · DOI：10.1002/mp.70591 · 研究领域：Advanced Radiotherapy Techniques、Radiation Therapy and Dosimetry、Boron Compounds in Chemistry

Abstract Background In 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. Methods The 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 of the primary CTV, 70 Gy(RBE), and elective CTV, 54.25 Gy(RBE), with a 1 mm margin applied to the CTVs during optimization. Six initial positions per rCT were considered, randomly generated within ±7 mm in each translational directions relative to t...