Integrating aperture shape controller and machine learning prediction to improve gamma passing rates in lattice radiotherapy

Objective: This study proposes a workflow integrating the Aperture Shape Controller (ASC) in the Varian Eclipse system with a machine learning–based verification prediction model to improve gamma passing rates (GPRs) of LATTICE Radiotherapy (LRT) plans and reduce repeated verifications. Approach: LRT plans were generated on CT images from 20 patients (five each with head and neck, thoracic, abdominal, and bone metastases) using a Varian Halcyon v3.0. Twenty IMRT-based Non_ASC_LRT plans (nine fields each, 180 fields total) were created and re-optimized with ASC set to “Very High,” producing ASC_LRT plans. All plans underwent portal dosimetry. Plan complexity features were extracted from DICOM files, and dosimetric parameters from DVHs. An eXtreme Gradient Boosting (XGBoost) classifier was trained 5 times with cross-validation on a balanced dataset of 196 fields generated by under-sampling and SMOTE, and subsequently applied to failed fields in Non_ASC_LRT plans to evaluate its potential guidance for ASC application. Main results: ASC optimization improved GPR in fields with baseline values <98% (mean increase 1.95%, p<0.05), while overall GPR showed no significant change. Dosimetric parameters (Dmax, Dmean_target, Dmean_vertex, and valley-to-peak dose ratio (VPDR)) were not significantly affected, though VPDR showed a slight overall increase. Over five training rounds, the XGBoost model achieved an average accuracy of 94.38%, sensitivity of 94.60%, specificity of 94.13%, precision of 93.57%, and an F1-score of 93.98%. All six failed fields in Non_ASC_LRT plans were correctly identified, and five passed verification after ASC optimization, reducing re-planning. Significance: Combining ASC with a prediction model effectively balances dosimetric quality and deliverability in LRT, enhances GPR in low-performing fields, and reduces verification workload, thereby improving clinical efficiency.