Wang Hesheng, Xue Jinyu, Qu Tanxia, Bernstein Kenneth, Chen Ting, Barbee David, Silverman Joshua S, Kondziolka Douglas
Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA.
Department of Neurosurgery, NYU Langone Medical Center, New York University, New York, New York, USA.
Med Phys. 2021 Sep;48(9):5522-5530. doi: 10.1002/mp.15110. Epub 2021 Jul 31.
Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases.
MATERIALS/METHODS: Twenty-eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB-approved study. 179 irradiated tumors included 42 that locally failed within one-year follow-up. Using SRS tumor volumes, radiomic features were calculated on T1-weighted contrast-enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis.
The logistic model with feature selection achieved ROC AUC of 0.82 ± 0.09 on 5-fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes (p < 0.05). The model was validated on independent holdout data with an AUC of 0.78.
Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases.
立体定向放射外科（SRS）已成为治疗脑转移瘤的重要手段。本研究的目的是探讨从计划磁共振（MR）图像和剂量图中提取的影像组学特征预测脑转移瘤SRS术后局部复发的可能性。
材料/方法：本研究经机构审查委员会批准，对28例接受伽玛刀（GK）放射外科治疗脑转移瘤的患者进行回顾性分析。179个接受照射的肿瘤中，42个在1年随访期内出现局部复发。利用SRS肿瘤体积，在用于治疗计划的T1加权对比增强MR图像和计划剂量图上计算影像组学特征。为每个肿瘤提取了125个关于肿瘤形状、剂量分布、MR信号强度和纹理的影像组学特征。构建具有自动特征选择功能的逻辑回归模型，以预测SRS术后肿瘤从局部控制到进展的情况。在嵌套交叉验证（CV）方案中，使用受试者操作特征（ROC）曲线进行特征选择和模型评估。通过单因素分析对所选影像组学特征与治疗结果之间的关联进行统计学评估。
具有特征选择功能的逻辑模型在5折交叉验证中的ROC曲线下面积（AUC）为0.82±0.09，预测局部复发的灵敏度为83%，特异度为70%。共选择了10个影像组学特征，包括1个形状特征、6个MR图像特征和3个剂量分布特征。这些特征与治疗结果显著相关（p<0.05）。该模型在独立验证数据上的AUC为0.78。
计划MR图像和剂量图的影像组学特征为脑转移瘤SRS提供了预后信息。基于影像组学特征构建的模型有望早期预测治疗后肿瘤局部复发，可能有助于脑转移瘤的个体化治疗。
