Leveraging Support Vector Regression, Radiomics and Dosiomics for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)
It addresses the need for accurate outcome prediction to assist patient selection and treatment adjustment in a novel radiotherapy domain, but is incremental as it applies existing methods to new data.
This study tackled the problem of predicting gross tumor volume changes in personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) by developing a multi-omics support vector regression model, achieving an R2 of 0.743 and an RRMSE of 0.022.
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.