A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans

Abstract

Stereotactic radiosurgery (SRS) for multiple brain metastases can be delivered with a single isocenter and non-coplanar arcs, achieving highly conformal dose distributions at the cost of extreme modulation of treatment machine parameters. As a result, SRS plans are at a higher risk of patient-specific quality assurance (PSQA) failure compared to standard treatments. This study aimed to develop a machine-learning (ML) model to predict the PSQA outcome (gamma passing rate, GPR) of SRS plans. Five hundred and ninety-two consecutive patients treated between 2020 and 2024 were selected. GPR analyses were performed using a 3%/1 mm criterion and a 95% action limit for each arc. Fifteen plan complexity metrics were used as input features to predict the GPR of an arc. A stratified and a time-series approach were employed to split the data into training (1555 arcs), validation (389 arcs), and test (486 arcs) sets. The ML model achieved a mean absolute error of 2.6% on the test set, with a 0.83% median residual value (measured/predicted). Lower values of the measured GPR tended to be overestimated. Sensitivity and specificity were 93% and 56%, respectively. ML models for virtual QA of SRS can be integrated into clinical practice, facilitating more efficient PSQA approaches.

Keywords

• machine learning
• patient-specific QA
• stereotactic radiosurgery
• HyperArc
• radiotherapy