Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada.
Department of Radiation Oncology, University of Toronto, Toronto, Canada.
J Neurooncol. 2021 Nov;155(2):181-191. doi: 10.1007/s11060-021-03866-9. Epub 2021 Oct 25.
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).
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.
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.
Quantitative analysis of conventional MRI sequences can effectively demarcate GBM PTR from LGG, which is otherwise indistinguishable on visual estimation.
胶质母细胞瘤（GBM）的瘤周区（PTR）在 T2W 上表现为高信号，由微观肿瘤和水肿组成。浸润性低级别胶质瘤（LGG）包含的肿瘤细胞在 MRI 上与 GBM PTR 相似。本研究旨在探讨基于放射组学的方法是否可以区分这两组（肿瘤和水肿与仅肿瘤）。
本研究纳入了使用 1.5T MRI 成像的 GBM 和 LGG 患者。GBM PTR 和 LGG 的图像数据由 T2W 高信号引导手动分割。从 T1W 对比、T2W-FLAIR 和弥散加权成像序列中确定了一组 91 个一阶和纹理特征。应用过滤技术，共获得 3822 个特征。采用不同的特征降维技术，然后使用四个机器学习分类器构建后续模型。采用留一法交叉验证评估分类器性能。
分析纳入了 42 例 GBM 和 36 例 LGG。使用 AdaBoost 分类器结合所有特征，获得了最佳性能，敏感性、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，AUC 范围为 0.87 至 0.92。F 检验评估的结果显示，选择的特征最一致，有 3 个 T1W 对比纹理特征在 90%以上的情况下被选中。
常规 MRI 序列的定量分析可以有效地将 GBM PTR 与 LGG 区分开来，而仅凭视觉评估则无法区分两者。
