Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710038, PR China.
College of Basic Medicine, Fourth Military Medical University, Xi'an, Shaanxi, 710032, PR China.
Eur J Radiol. 2021 Jan;134:109467. doi: 10.1016/j.ejrad.2020.109467. Epub 2020 Dec 3.
In populations without contrast enhancement, the imaging features of atypical brain parenchyma inflammations can mimic those of grade II gliomas. The aim of this study was to assess the value of the conventional MR-based radiomics signature in differentiating brain inflammation from grade II glioma.
Fifty-seven patients (39 patients with grade II glioma and 18 patients with inflammation) were divided into primary (n = 44) and validation cohorts (n = 13). Radiomics features were extracted from T-weighted images (TWI) and T-weighted images (TWI). Two-sample t-test and least absolute shrinkage and selection operator (LASSO) regression were adopted to select features and build radiomics signature models for discriminating inflammation from glioma. The predictive performance of the models was evaluated via area under the receiver operating characteristic curve (AUC) and compared with the radiologists' assessments.
Based on the primary cohort, we developed TWI, TWI and combination (TWI + TWI) models for differentiating inflammation from glioma with 4, 8, and 5 radiomics features, respectively. Among these models, TWI and combination models achieved better diagnostic efficacy, with AUC of 0.980, 0.988 in primary cohort and that of 0.950, 0.925 in validation cohort, respectively. The AUCs of radiologist 1's and 2's assessments were 0.661 and 0.722, respectively.
The signature based on radiomics features helps to differentiate inflammation from grade II glioma and improved performance compared with experienced radiologists, which could potentially be useful in clinical practice.
在没有对比增强的人群中，非典型脑实质炎症的影像学特征可能类似于二级胶质瘤。本研究旨在评估基于常规磁共振成像的放射组学特征在区分脑炎症与二级胶质瘤中的价值。
57 例患者（39 例为二级胶质瘤患者，18 例为炎症患者）分为原发性（n=44）和验证队列（n=13）。从 T1 加权图像（T1WI）和 T2 加权图像（T2WI）中提取放射组学特征。采用两样本 t 检验和最小绝对值收缩和选择算子（LASSO）回归筛选特征并构建放射组学特征模型，以区分炎症和胶质瘤。通过受试者工作特征曲线（ROC）下面积（AUC）评估模型的预测性能，并与放射科医生的评估进行比较。
基于原发性队列，我们开发了用于区分炎症与胶质瘤的 T1WI、T2WI 和组合（T1WI+T2WI）模型，分别具有 4、8 和 5 个放射组学特征。在这些模型中，T1WI 和组合模型具有更好的诊断效能，在原发性队列中的 AUC 分别为 0.980、0.988，在验证队列中的 AUC 分别为 0.950、0.925。放射科医生 1 和 2 的评估 AUC 分别为 0.661 和 0.722。
基于放射组学特征的特征有助于区分炎症和二级胶质瘤，与经验丰富的放射科医生相比，其性能有所提高，这在临床实践中可能具有潜在的应用价值。
