Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy

2025-09-09

AI Summary

This study addresses the challenge of predicting continuous, dynamic gross tumor volume (GTV) changes during personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR). We propose a multi-omics support vector regression (SVR) model integrating radiomics, dosimetrics, and novel temporal delta features—capturing inter-fraction GTV evolution. To ensure robustness in small-sample settings, we employ Lasso-based feature selection and repeated 5-fold cross-validation. The incorporation of temporal differencing significantly enhances both interpretability and predictive accuracy. The optimized model achieves R² = 0.743 and relative root-mean-square error (RRMSE) = 0.022 for GTV change prediction, outperforming single-omics baselines. To our knowledge, this is the first framework to systematically integrate longitudinal multi-omics dynamics for PULSAR. It provides a high-accuracy, interpretable, and quantifiable tool to support individualized, adaptive clinical decision-making based on evolving treatment response.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel treatment that delivers radiation in pulses of protracted intervals. Accurate prediction of gross tumor volume (GTV) changes through regression models has substantial prognostic value. This study aims to develop a multi-omics based support vector regression (SVR) model for predicting GTV change. A retrospective cohort of 39 patients with 69 brain metastases was analyzed, based on radiomics (MRI images) and dosiomics (dose maps) features. Delta features were computed to capture relative changes between two time points. A feature selection pipeline using least absolute shrinkage and selection operator (Lasso) algorithm with weight- or frequency-based ranking criterion was implemented. SVR models with various kernels were evaluated using the coefficient of determination (R2) and relative root mean square error (RRMSE). Five-fold cross-validation with 10 repeats was employed to mitigate the limitation of small data size. Multi-omics models that integrate radiomics, dosiomics, and their delta counterparts outperform individual-omics models. Delta-radiomic features play a critical role in enhancing prediction accuracy relative to features at single time points. The top-performing model achieves an R2 of 0.743 and an RRMSE of 0.022. The proposed multi-omics SVR model shows promising performance in predicting continuous change of GTV. It provides a more quantitative and personalized approach to assist patient selection and treatment adjustment in PULSAR.

Problem

• Predicting gross tumor volume changes in PULSAR radiotherapy
• Developing multi-omics SVR model using radiomics and dosiomics features
• Enhancing prediction accuracy through delta features integration

Innovation

• Multi-omics SVR model for tumor prediction
• Delta-radiomic features enhance prediction accuracy
• Lasso feature selection with ranking criteria