Leaf-Specific Classification of Multi-Leaf Collimator Positioning Errors in Volumetric Modulated Arc Therapy Using a Convolutional Neural Network

Background/Objectives: Multi-leaf collimator (MLC) positioning accuracy critically affects delivered dose fidelity in volumetric modulated arc therapy (VMAT), yet conventional gamma-based quality assurance (QA) provides only plan-level pass/fail outcomes without leaf-specific error localization. This study developed and validated a convolutional neural network (CNN) framework that classifies the magnitude and direction of individual MLC leaf positioning errors directly from fluence map data. Methods: Three patient cohorts were analyzed: 20 prostate cancer patients for model development under an 8:1:1 train/validation/test split and 20 additional prostate and 10 head and neck (H&N) patients reserved for external validation. For inner MLC leaves 21–40, systematic offsets from −5 mm to +5 mm in 1.0 mm increments were independently applied to the two leaf banks, yielding 121 error combinations per leaf. A CNN was trained as a 121-class classifier on two-channel inputs pairing the reference and error-induced fluence map regions and was compared against three tree-based baselines using five-fold cross-validation. Results: The CNN achieved 97.00% accuracy on the internal test set and 96.54 ± 0.43% accuracy across the five patient-level cross-validation folds. Across all test samples, 99.88% and 99.83% of predictions were within 1 mm of the true offset for Bank A and Bank B, respectively, well within the AAPM TG-142 1 mm MLC positioning tolerance. External validation yielded 96.19% accuracy on the additional prostate cohort and 93.72% on the H&N cohort, suggesting reproducibility within the same anatomical site and potential robustness across anatomically distinct treatment sites within a single-institution dataset. Conclusions: The proposed CNN framework demonstrates the feasibility of leaf-specific identification of MLC positioning errors in both magnitude and direction from simulated fluence maps. These findings support further investigation using physically measured fluence data for future clinical translation.