Research on error classification in gamma analysis on the basis of dosimetric feature engineering and deep learning

<i>Purpose</i>. Gamma analysis serves as a critical safety assurance tool in radiotherapy, yet its broader clinical implementation remains constrained by insufficient error cause determination. To address this limitation, this study proposes a gamma passing rate (GPR) prediction method with error classification capabilities by integrating dosimetric feature engineering with a dose prediction model.<i>Method</i>. The study cohort comprised 26 clinical cases, with 6 cases (1,515 static segments generated from volumetric modulated arc therapy (VMAT) plans) allocated for model training and 20 cases (10 step-and-shoot plans with 415 segments and 10 VMAT plans) allocated for testing. Measurements were performed using a MatriXX chamber array at a gantry angle of 0 degrees. Data was acquired segment-by-segment for step-and-shoot plans and via integration for VMAT plans, respectively. A dosimetric feature engineering protocol was used to partition each static segment into five distinct regions (Region 1-5) on the basis of physical characteristics and error susceptibility patterns. These regional dose distributions served as both model inputs and independent variables for error analysis. A GAN-based model was trained to predict segment doses, which were subsequently aggregated for plan-level GPR calculations. Model accuracy was first validated by statistically analyzing GPR differences between measurements and predictions across various plan types in the test set, followed by assessing dose discrepancies at failure and passing points for both measured and predicted values.<i>Results</i>. The predicted GPR was 70.26% ± 13.07% for segments, 93.53% ± 2.06% for step-and-shoot plans, and 92.61% ± 4.67% for VMAT plans, with corresponding measured values of 74.47% ± 10.06%, 96.35% ± 1.82%, and 91.60% ± 4.05%, respectively. Regional dose analysis revealed statistically significant differences (p < 0.05) in the measured values for Regions 2-5, with classification AUC values of 0.69, 0.64, 0.65, and 0.63, respectively. The predicted values showed comparable performance for Region 2 with AUC of 0.66, whereas Regions 3-5 demonstrated AUCs of 0.50, 0.59, and 0.57 respectively.<i>Conclusions</i>. The integrated approach enables accurate GPR prediction while providing actionable error localization at the control point level. The quantitative error source analysis offers valuable guidance for modifying high-risk treatment plans and demonstrates significant potential for enhancing clinical radiotherapy quality assurance protocols.