Gradient-Based Radiomics for Outcome Prediction and Decision-Making in PULSAR: A Preliminary Study.

Purpose: Personalized ultrafractionated stereotactic adaptive radiation therapy (PULSAR) has emerged as an innovative method for delivering high-dose radiation over extended intervals, adapting treatment based on the patient's response. However, current adaptation largely relies on physicians' experience and tumor size assessment, underscoring the need for a data-driven approach to improve outcome prediction and support decision-making. Materials and Methods: We analyzed 69 lesions from 39 patients undergoing PULSAR treatment. Gradient-based features, including gradient magnitude, radial gradient, and radial deviation, were extracted from both intratumoral and peritumoral regions, with the latter further divided into octant subregions. Support vector machine models were developed using features from first magnetic resonance imaging (MRI), second MRI, and delta mode (change between the 2). An ensemble feature selection (EFS) model was then created by combining the features of the top-performing individual models. The approach was validated on a non-PULSAR cohort (37 lesions from 23 patients) treated with standard fractionated stereotactic radiation therapy. Results: The EFS model shows strong predictive performance in determining whether tumor volume reduction exceeds 20% at the 3-month postradiation time point. Features derived from octant subregions exhibit significantly better prediction than those from the core or entire margin. Pretreatment features (from first MRI) generally outperform second MRI and delta-mode features, while the inclusion of 1 delta feature in the EFS model enhances performance. In the non-PULSAR cohort, the gradient-based approach outperforms conventional radiomics, demonstrating its strong generalizability. Conclusion: Our gradient-based radiomics approach, combining spatial segmentation and temporal features, significantly enhances treatment response prediction in PULSAR therapy. Its superior performance compared to conventional radiomics, coupled with its effectiveness in both PULSAR and non-PULSAR cohorts, highlights its potential as a robust tool for personalized treatment planning in neuro-oncology, applicable to both photon and particle therapies.