Division of Radiation Oncology, McGill University Health Centre, Montreal, Canada.
Ecole de Technologie Supérieure, Montréal, Canada.
Med Biol Eng Comput. 2018 Dec;56(12):2287-2300. doi: 10.1007/s11517-018-1858-4. Epub 2018 Jun 19.
We propose a multiscale texture features based on Laplacian-of Gaussian (LoG) filter to predict progression free (PFS) and overall survival (OS) in patients newly diagnosed with glioblastoma (GBM). Experiments use the extracted features derived from 40 patients of GBM with T1-weighted imaging (T1-WI) and Fluid-attenuated inversion recovery (FLAIR) images that were segmented manually into areas of active tumor, necrosis, and edema. Multiscale texture features were extracted locally from each of these areas of interest using a LoG filter and the relation between features to OS and PFS was investigated using univariate (i.e., Spearman's rank correlation coefficient, log-rank test and Kaplan-Meier estimator) and multivariate analyses (i.e., Random Forest classifier). Three and seven features were statistically correlated with PFS and OS, respectively, with absolute correlation values between 0.32 and 0.36 and p < 0.05. Three features derived from active tumor regions only were associated with OS (p < 0.05) with hazard ratios (HR) of 2.9, 3, and 3.24, respectively. Combined features showed an AUC value of 85.37 and 85.54% for predicting the PFS and OS of GBM patients, respectively, using the random forest (RF) classifier. We presented a multiscale texture features to characterize the GBM regions and predict he PFS and OS. The efficiency achievable suggests that this technique can be developed into a GBM MR analysis system suitable for clinical use after a thorough validation involving more patients. Graphical abstract Scheme of the proposed model for characterizing the heterogeneity of GBM regions and predicting the overall survival and progression free survival of GBM patients. (1) Acquisition of pretreatment MRI images; (2) Affine registration of T1-WI image with its corresponding FLAIR images, and GBM subtype (phenotypes) labelling; (3) Extraction of nine texture features from the three texture scales fine, medium, and coarse derived from each of GBM regions; (4) Comparing heterogeneity between GBM regions by ANOVA test; Survival analysis using Univariate (Spearman rank correlation between features and survival (i.e., PFS and OS) based on each of the GBM regions, Kaplan-Meier estimator and log-rank test to predict the PFS and OS of patient groups that grouped based on median of feature), and multivariate (random forest model) for predicting the PFS and OS of patients groups that grouped based on median of PFS and OS.
我们提出了一种基于拉普拉斯算子（LoG）滤波器的多尺度纹理特征，用于预测新诊断为胶质母细胞瘤（GBM）患者的无进展生存期（PFS）和总生存期（OS）。实验使用从 40 名 GBM 患者的 T1 加权成像（T1-WI）和液体衰减反转恢复（FLAIR）图像中提取的特征，这些特征通过手动分割成活跃肿瘤、坏死和水肿区域。使用 LoG 滤波器从每个感兴趣区域局部提取多尺度纹理特征，并使用单变量（即 Spearman 秩相关系数、对数秩检验和 Kaplan-Meier 估计器）和多变量分析（即随机森林分类器）研究特征与 OS 和 PFS 的关系。有三个和七个特征分别与 PFS 和 OS 具有统计学相关性，绝对相关值在 0.32 到 0.36 之间，p 值均小于 0.05。仅从活跃肿瘤区域得出的三个特征与 OS 相关（p 值均小于 0.05），危险比（HR）分别为 2.9、3 和 3.24。使用随机森林（RF）分类器，联合特征对预测 GBM 患者的 PFS 和 OS 的 AUC 值分别为 85.37%和 85.54%。我们提出了一种多尺度纹理特征来描述 GBM 区域，并预测 GBM 患者的 PFS 和 OS。通过随机森林（RF）分类器，预测 GBM 患者的 PFS 和 OS 的 AUC 值分别为 85.37%和 85.54%，这表明该技术具有较高的效率，在经过更广泛的患者验证后，该技术可以开发成为一种适合临床使用的 GBM 磁共振分析系统。
