Proton radiography interpretation with artificial intelligence for treatment deviation detection in proton therapy

作者：G Bernardini, Arthur Galapon, Gabriel Guterres Marmitt, Jeffrey Free, Peter M. A. van Ooijen, Johannes A. Langendijk, Stefan Both · 发表于：Physics and Imaging in Radiation Oncology · 年份：2025 · DOI：10.1016/j.phro.2025.100872 · 被引用次数：1 · 研究领域：Radiation Therapy and Dosimetry、Space Technology and Applications、Advanced Radiotherapy Techniques

Background and purpose Treatment deviations from patient setup errors, anatomical changes, and uncertainties in proton range estimation degrade dose conformity in proton therapy. Adaptive proton therapy (APT) mitigates these deviations by monitoring and adjusting treatment plans. Proton radiography (PR) offers direct proton range information, making it a promising method to detect such deviations. In this study, we developed and evaluated an artificial intelligence (AI) PR tool for automated interpretation and classification of deviations. Materials and methods Computed Tomography (CT) scans from 32 head-and-neck cancer patients were synthetically modified to simulate setup errors (±2–4 mm), calibration curve errors (±3–5 % for fat/soft tissue, ±7–11 % for bone), and anatomical changes (±2–12 mm mimicking weight variations). PR simulations were performed using OpenREGGUI to generate integral depth dose (IDD) curves and range shift maps (RSMs) across 260 × 260 mm 2 PR fields, resulting in 14,503 RSMs. A convolutional neural network (EfficientNet-v2-M) was trained from scratch for multi-label classification. Performance was evaluated on the synthetic dataset and an independent clinical dataset of 22 patients who underwent plan adaptation. Results The CNN classified treatment deviations within one second per image. On the synthetic dataset, it achieved 97% precision, 92% recall, 93% F1-score, and 92% F2-score. On the clinical validation set, it maintained high performance metric...