Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Military Medical University of PLA Airforce (Fourth Military Medical University), Shaanxi, P.R. China.
Department of Biomedical Engineering, Military Medical University of PLA Airforce (Fourth Military Medical University), Shaanxi, P.R. China.
J Magn Reson Imaging. 2018 Dec;48(6):1518-1528. doi: 10.1002/jmri.26010. Epub 2018 Mar 23.
Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays.
PURPOSE/HYPOTHESIS: To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps.
Retrospective; radiomics.
A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively.
FIELD STRENGTH/SEQUENCE: 3.0T MRI/T -weighted images before and after contrast-enhanced, T -weighted, multi-b-value diffusion-weighted and 3D arterial spin labeling images.
After multiparametric MRI preprocessing, high-throughput features were derived from patients' volumes of interests (VOIs). The support vector machine-based recursive feature elimination was adopted to find the optimal features for low-grade glioma (LGG) vs. high-grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency.
Student's t-test or a chi-square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist.
Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI.
Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision-making for patients with varied glioma grades.
3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528.
准确的胶质瘤分级在患者的临床管理中起着重要作用，也是目前分子分层的基础。
目的/假设：验证从多参数 MRI 提取的放射组学特征优于胶质瘤分级，并评估不同 MRI 序列或参数图的分级潜力。
回顾性；放射组学。
共有 153 名患者，分别包括 42、33 和 78 名 II、III 和 IV 级胶质瘤患者。
磁场强度/序列：3.0T MRI/T1 加权成像增强前后、T1 加权、多 b 值扩散加权和 3D 动脉自旋标记图像。
在多参数 MRI 预处理后，从患者的感兴趣容积（VOI）中提取高通量特征。采用基于支持向量机的递归特征消除方法，寻找低级别胶质瘤（LGG）与高级别胶质瘤（HGG）和 3 级与 4 级胶质瘤分类任务的最佳特征。然后使用最佳特征建立支持向量机（SVM）分类器。使用准确性和曲线下面积（AUC）评估分级效率。
在不同的临床特征上应用学生 t 检验或卡方检验，以确认组间是否存在显著差异。
LGG 和 HGG 组患者的年龄差异有统计学意义（P<0.01）。对于每个患者，从多参数 MRI 的 10 个 VOI 中提取了 420 个纹理和 90 个直方图参数。使用 30 和 28 个最佳特征分别建立了用于将 LGG 与 HGG 分类和将 3 级与 4 级分类的 SVM 模型。LGG 与 HGG 分类的准确率/AUC 分别为 96.8%/0.987，3 级与 4 级分类的准确率/AUC 分别为 98.1%/0.992，这优于使用直方图参数或单一序列 MRI。
纹理特征比直方图参数更有效地对胶质瘤进行无创分级。多参数 MRI 的联合应用提供了更高的分级效率。所提出的放射组学策略可以为不同级别胶质瘤患者的临床决策提供便利。
3 技术功效：第 2 阶段 J. 磁共振成像 2018；48：1518-1528。
