Jeong Jiwoong, Wang Liya, Ji Bing, Lei Yang, Ali Arif, Liu Tian, Curran Walter J, Mao Hui, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Quant Imaging Med Surg. 2019 Jul;9(7):1201-1213. doi: 10.21037/qims.2019.07.01.
Glioblastoma is the most aggressive brain tumor with poor prognosis. The purpose of this study is to improve the tissue characterization of these highly heterogeneous tumors using delta-radiomic features of images from dynamic susceptibility contrast enhanced (DSC) magnetic resonance imaging (MRI).
Twenty-five patients with histopathologically confirmed to be 13 high-grade (HG) and 12 low-grade (LG) gliomas who underwent the standard brain tumor MRI protocol, including DSC MRI, were included. Tumor regions on all DSC MRI images were registered to and contoured in T2-weighted fluid-attenuated inversion recovery (FLAIR) images. These contours and its contralateral regions of the normal tissue were used to extract delta-radiomic features before applying feature selection. The most informative and non-redundant features were selected to train a random forest to differentiate HG and LG gliomas. Then a leave-one-out cross-validation random forest was applied to classify these tumors for grading. Finally, a majority-voting method was applied to reduce binarization bias and to combine the results of various feature lists.
Analysis of the predictions showed that the reported method consistently predicted the tumor grade of 24 out of 25 patients correctly (0.96). Finally, the mean prediction accuracy was 0.950±0.091 for HG and 0.850±0.255 for LG. The area under the receiver operating characteristic curve (AUC) was 0.94.
This study shows that delta-radiomic features derived from DSC MRI data can be used to characterize and determine the tumor grades. The radiomic features from DSC MRI may be used to elucidate the underlying tumor biology and response to therapy.
胶质母细胞瘤是最具侵袭性的脑肿瘤，预后较差。本研究的目的是利用动态磁敏感对比增强（DSC）磁共振成像（MRI）图像的增量放射组学特征，改善这些高度异质性肿瘤的组织特征。
纳入25例经组织病理学证实为13例高级别（HG）和12例低级别（LG）胶质瘤的患者，这些患者接受了包括DSC MRI在内的标准脑肿瘤MRI检查。所有DSC MRI图像上的肿瘤区域被配准到T2加权液体衰减反转恢复（FLAIR）图像并进行轮廓勾画。在进行特征选择之前，利用这些轮廓及其正常组织的对侧区域提取增量放射组学特征。选择最具信息性和非冗余的特征来训练随机森林，以区分HG和LG胶质瘤。然后应用留一法交叉验证随机森林对这些肿瘤进行分级分类。最后，应用多数投票法减少二值化偏差并合并各种特征列表的结果。
预测分析表明，所报道的方法一致正确地预测了25例患者中的24例肿瘤分级（0.96）。最后，HG的平均预测准确率为0.950±0.091，LG为0.850±0.255。受试者操作特征曲线（AUC）下的面积为0.94。
本研究表明，从DSC MRI数据中提取的增量放射组学特征可用于表征和确定肿瘤分级。DSC MRI的放射组学特征可能有助于阐明潜在的肿瘤生物学特性和对治疗的反应。
