用多组学特征预测脑转移瘤放疗中体积变化，提升个性化治疗精准度。
Leveraging Support Vector Regression, Radiomics and Dosiomics for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)
- 融合影像组学、剂量组学及变化特征，构建支持向量回归模型。
- 最优模型预测准确率R2达0.743，相对均方根误差仅0.022。
- 适用于需要动态调整治疗方案的精准放疗患者评估。
个性化超分割立体定向自适应放疗（PULSAR）是一种在延长间隔内分次给药的新疗法。通过回归模型准确预测大肿瘤体积（GTV）变化具有重要预后价值。本研究旨在开发基于多组学的支持向量回归（SVR）模型以预测GTV变化。分析了39例患者的69个脑转移病灶，利用MRI影像的影像组学特征和剂量图中的剂量组学特征，计算时间点间的差值特征（delta features）以捕捉动态变化。采用基于最小绝对收缩与选择算子（Lasso）算法的特征选择流程，结合权重或频率排序标准。对比多种核函数的SVR模型性能，使用决定系数（R²）和相对均方根误差（RRMSE）评估。采用五折交叉验证并重复10次，缓解小样本局限性。整合影像组学、剂量组学及其差值特征的多组学模型优于单一组学模型；差值影像组学特征显著提升预测精度。最优模型达到R²=0.743，RRMSE=0.022。所提多组学SVR模型在预测GTV连续变化方面表现良好，为PULSAR中患者筛选与治疗调整提供更定量、个性化的支持。
原文摘要 · Abstract (English)
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.
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