Med Phys. 2025 Dec;52(12):e70183. doi: 10.1002/mp.70183.
ABSTRACT
BACKGROUND: Compared to photon therapy (XT), proton therapy (PT) can often reduce normal tissue toxicity for head and neck (HN) cancer patients, despite being a limited resource. On the other hand, clinical decision-making process to select between PT and XT (e.g., treatment planning and then plan evaluation for comparing normal tissue complication probabilities (NTCP) between XT and PT) is time-consuming and resource demanding.
PURPOSE: This study aims to develop and validate the feasibility of an artificial intelligence (AI)-based automated method for efficient patient selection between PT and XT.
METHODS: A heterogeneous cohort of 104 bilateral HN patients with auto-planned PT and XT plans was analyzed, covering diverse tumor subsites and prescription dose levels. To ensure accurate dose and NTCP prediction, a joint-modality prediction framework was developed, incorporating a 3D attention-gated U-net with a multi-constrained loss function. A stratified 10-fold cross-validation strategy was employed to evaluate and compare model performance. The NTCP differences between XT and PT for grade II/III xerostomia/dysphagia exceeding certain thresholds are used to select patients for PT according to the Landelijk Indicatie Protocol Protonentherapie (versie 2.2) (LIPPv2.2).
RESULTS: AI-assisted patient selection process took about 10.1 s per patient. Our method achieved an accuracy of 85.58% and a weighted accuracy of 81.11% in patient selection. For dysphagia grades ≥ 2 and ≥ 3, the predicted results exhibited consistent selection with the ground truth in 86.54% and 89.42% of cases, respectively. Compared to previous models, the average ΔNTCP prediction error (ΔNTCP ground truth-ΔNTCP predicted, mean ± SD) of the proposed method was 1.47 ± 1.80%, statistically lower than U-net (1.67 ± 2.20%) and hierarchically densely connected U-net (2.34 ± 3.25%). Moreover, the joint-modality prediction of PT and XT dose distributions using Attention U-net achieved comparable performance to separate single-modality predictions.
CONCLUSION: This study highlights the potential of a novel AI-assisted framework with joint-modality prediction to enhance efficiency and precision for proton-photon patient selection in the heterogeneous dataset, demonstrating the generalizability and robustness of the proposed approach.
PMID:41345820 | DOI:10.1002/mp.70183
