Physics-informed machine learning for predicting MLC and gantry errors in VMAT: a feature engineering approach

Abstract

Background

This study presents a physics-informed, feature-engineered machine learning (ML) framework to predict multileaf collimator (MLC) and gantry positional errors in volumetric modulated arc therapy (VMAT)

Methods

Data from 32 VMAT trajectory logs (TrueBeam linac, HD120 MLC) were synchronized with DICOMRT plans to extract delivery dynamics. Novel physics-based parameters were introduced: a friction factor, an enhanced gravity vector, and MLC speed-normalized features. Three ML models XGBoost, LightGBM, and deep neural networks (DNNs) were optimized using Optuna and trained on trajectory log and DICOM-RT-derived datasets. Feature importance was evaluated via Spearman correlation, mutual information, and SHapley Additive Explanations (SHAP).

Results

Systematic discrepancies between DICOM-RT and trajectory log data were identified, with mean absolute deviations of 7.0 % (MLC speed), 8.0 % (gantry speed), and 8.5 % (dose rate). MLC speed emerged as the dominant predictor (Spearman: r_s = 0.891), while friction and gravity features exhibited significant correlations (r_s = 0.46 and 0.33, respectively). Mutual information revealed non-monotonic dependencies between gantry error and gantry angle (score: 0.34). LightGBM and XGBoost achieved superior MLC error prediction (MAE: 0.0019 mm, RMSE: 0.0027 mm), capturing > 90 % of observed errors, while DNNs lagged by 30 %. Engineered features reduced residual errors by 30 %. Gantry error predictions showed lower accuracy (MAE: 0.012°–0.015°). SHAP analysis highlighted physics-driven features as top contributors.

Conclusion

This work underscores the critical role of domain knowledge in ML for radiotherapy, achieving a 30% error reduction through physics-based feature engineering. The findings advocate for prioritizing feature space exploration alongside model optimization to enhance VMAT quality assurance.