本研究旨在使用机器学习 (ML) 算法从 MRI 中提取放射组学特征，并将其与临床特征相结合，为接受立体定向全身放疗 (SBRT) 的脊柱转移瘤患者建立反应预测模型。我院2018年7月至2023年4月期间招募。我们使用修订后的实体瘤疗效评估标准（1.1 版）评估了他们对治疗的反应。病变分为进行性疾病（PD）组和非PD组。从 T1 加权图像 (T1WI)、T2 加权图像 (T2WI) 和脂肪抑制 T2WI 序列中提取放射组学特征。特征选择涉及类内相关系数、最小冗余最大相关性、最小绝对收缩和选择算子方法。采用 13 种 ML 算法来构建放射组学预测模型。整合临床、常规成像和放射组学特征来开发组合模型。使用受试者工作特征（ROC）曲线分析评估模型性能，并使用决策曲线分析评估临床价值。我们纳入了 194 名患者，其中非 PD 组有 142 个病变（73.2%），PD 组有 52 个病变（26.8%）。 PD组。每个感兴趣区域生成 2264 个特征。临床模型表现出中等预测价值（ROC 曲线下面积，AUC = 0.733），而放射组学模型表现出更好的性能（AUC = 0.745-0.825）。组合模型取得了最佳性能（AUC = 0.828）。基于 MRI 的放射组学模型对接受 SBRT 的脊柱转移瘤患者的治疗结果表现出有价值的预测能力。放射组学预测模型有可能有助于临床决策并改善临床决策接受 SBRT 的脊柱转移瘤患者的预后。• 立体定向放射治疗可有效提供高剂量的放射治疗脊柱转移瘤。 • 准确预测治疗结果具有至关重要的临床意义。 • 基于 MRI 的放射组学模型在预测治疗结果方面表现出良好的性能。© 2023。欧洲放射学会 (ESR)。

This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT).Patients with spinal metastases who were treated using SBRT at our hospital between July 2018 and April 2023 were recruited. We assessed their response to treatment using the revised Response Evaluation Criteria in Solid Tumors (version 1.1). The lesions were categorized into progressive disease (PD) and non-PD groups. Radiomics features were extracted from T1-weighted image (T1WI), T2-weighted image (T2WI), and fat-suppression T2WI sequences. Feature selection involved intraclass correlation coefficients, minimal-redundancy-maximal-relevance, and least absolute shrinkage and selection operator methods. Thirteen ML algorithms were employed to construct the radiomics prediction models. Clinical, conventional imaging, and radiomics features were integrated to develop combined models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the clinical value was assessed using decision curve analysis.We included 194 patients with 142 (73.2%) lesions in the non-PD group and 52 (26.8%) in the PD group. Each region of interest generated 2264 features. The clinical model exhibited a moderate predictive value (area under the ROC curve, AUC = 0.733), while the radiomics models demonstrated better performance (AUC = 0.745-0.825). The combined model achieved the best performance (AUC = 0.828).The MRI-based radiomics models exhibited valuable predictive capability for treatment outcomes in patients with spinal metastases undergoing SBRT.Radiomics prediction models have the potential to contribute to clinical decision-making and improve the prognosis of patients with spinal metastases undergoing SBRT.• Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases. • Accurate prediction of treatment outcomes has crucial clinical significance. • MRI-based radiomics models demonstrated good performance to predict treatment outcomes.© 2023. European Society of Radiology (ESR).