MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region
MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region
复制标题
DOI：
10.1007/s11060-021-03866-9
复制
发表时间：
2021-10-25
影响因子：
3.9
通讯作者：
Czarnota, Gregory J.
中科院分区：
文献类型：
--
作者：
Malik, Nauman;Geraghty, Benjamin;Czarnota, Gregory J.
胶质母细胞瘤瘤周区（PTR）表现为T2 WI高信号，由显微镜下肿瘤和水肿组成。浸润性低级别胶质瘤（LGG）包括在MRI上看起来类似于GBM PTR的肿瘤细胞。这项工作探讨了基于放射学的方法是否可以区分两组（肿瘤和水肿与单独的肿瘤）。方法采用1.5T MRI对GBM和LGG患者进行检查。GBM PTR和LGG病例的图像数据在T2 W高信号引导下手动分割。一组91个一阶和纹理特征，确定从每个T1 W对比，T2 W-FLAIR，扩散加权成像序列。应用过滤技术，共获得3822个特征。采用不同的特征约简技术，并使用四个机器学习分类器构建后续模型。使用留一法交叉验证来评估分类器性能。结果GBM 42例，LGG 36例。使用AdaBoost分类器获得最佳性能，使用所有特征，灵敏度，特异性，准确性和曲线下面积（AUC）分别为91%，86%，89%和0.96。在特征选择技术中，递归特征消除技术的结果最好，其AUC范围为0.87至0.92。用F检验进行的评估导致在超过90%的实例中选择了最一致的特征选择，具有3个T1 W对比度纹理特征。结论常规MRI序列的定量分析可以有效区分视觉上难以区分的GBM PTR和LGG。
Background The peritumoral region (PTR) of glioblastoma (GBM) appears as a T2W-hyperintensity and is composed of microscopic tumor and edema. Infiltrative low grade glioma (LGG) comprises tumor cells that seem similar to GBM PTR on MRI. The work here explored if a radiomics-based approach can distinguish between the two groups (tumor and edema versus tumor alone). Methods Patients with GBM and LGG imaged using a 1.5 T MRI were included in the study. Image data from cases of GBM PTR, and LGG were manually segmented guided by T2W hyperintensity. A set of 91 first-order and texture features were determined from each of T1W-contrast, and T2W-FLAIR, diffusion-weighted imaging sequences. Applying filtration techniques, a total of 3822 features were obtained. Different feature reduction techniques were employed, and a subsequent model was constructed using four machine learning classifiers. Leave-one-out cross-validation was used to assess classifier performance. Results The analysis included 42 GBM and 36 LGG. The best performance was obtained using AdaBoost classifier using all the features with a sensitivity, specificity, accuracy, and area of curve (AUC) of 91%, 86%, 89%, and 0.96, respectively. Amongst the feature selection techniques, the recursive feature elimination technique had the best results, with an AUC ranging from 0.87 to 0.92. Evaluation with the F-test resulted in the most consistent feature selection with 3 T1W-contrast texture features chosen in over 90% of instances. Conclusions Quantitative analysis of conventional MRI sequences can effectively demarcate GBM PTR from LGG, which is otherwise indistinguishable on visual estimation.