Feasibility of quantifying intra-fractional patient positional error for head and neck IMRT using radiomic features of EPID-measured fluence maps: a phantom study.

This study aimed to evaluate the feasibility of estimating patient positional errors during head-and-neck fixed-field intensity-modulated radiation therapy (IMRT) using a machine-learning (ML) regression models based on radiomics features of patient-transmitted fluence maps acquired by an electronic portal imaging device (EPID), as a proof-of-concept toward quantitative intrafractional monitoring. Methods: A virtual target, spinal cord, and parotid glands were contoured on an RSVP phantom simulating nine fixed-field IMRT plans. Treatment plans were created with and without a 0.5-cm or 1.0-cm bolus placed on the phantom surface. For each field, phantom positional errors were simulated in the anterior-posterior (AP), left-right (LR), and superior-inferior (SI) directions at 1-mm intervals from -5 mm to 5 mm. Fluence maps were acquired using an EPID, and difference maps were generated between error and error-free cases. A total of 837 radiomics features were extracted. The dataset included 180 cases (with no bolus or 1.0-cm bolus) for training and 90 cases (with 0.5-cm bolus) for testing. Three ML regression models-random forest, AdaBoost and extra trees-were used. Positional error detection accuracy was evaluated using root mean square error (RMSE), mean absolute error (MAE) and Spearman's rank correlation coefficient. Results: In the test data, RMSE values ranged from 0.550-0.779 mm (AP), 0.440-0.493 mm (LR), and 0.616-0.689 mm (SI). Corresponding MAE values ranged from 0.394-0.584 mm, 0.351-0.392 mm, and 0.492-0.573 mm, respectively. Spearman correlation coefficients ranged from 0.890-0.946 (AP), 0.949-0.953 (LR), and 0.860-0.898 (SI). Conclusions: This phantom-based study suggests that radiomics-based machine-learning regression can quantitatively estimate translational positional-error magnitude from EPID-measured fluence differences under controlled conditions. These findings represent a proof-of-concept for the feasibility of intrafractional geometric monitoring rather than clinical validation. Further investigation using clinically realistic patient data, anatomical changes, and day-to-day machine variations is required before routine clinical implementation.