Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme

Abstract

One of the most common and aggressive malignant brain tumors is Glioblastoma multiforme. Despite the multimodality treatment such as radiation therapy and chemotherapy (temozolomide: TMZ), the median survival rate of glioblastoma patient is less than 15 months. In this study, we investigated the association between measures of spatial diversity derived from spatial point pattern analysis of multiparametric magnetic resonance imaging (MRI) data with molecular status as well as 12-month survival in glioblastoma. We obtained 27 measures of spatial proximity (diversity) via spatial point pattern analysis of multiparametric T1 post-contrast and T2 fluid-attenuated inversion recovery MRI data. These measures were used to predict 12-month survival status (≤12 or >12 months) in 74 glioblastoma patients. Kaplan-Meier with receiver operating characteristic analyses was used to assess the relationship between derived spatial features and 12-month survival status as well as molecular subtype status in patients with glioblastoma. Kaplan-Meier survival analysis revealed that 14 spatial features were capable of stratifying overall survival in a statistically significant manner. For prediction of 12-month survival status based on these diversity indices, sensitivity and specificity were 0.86 and 0.64, respectively. The area under the receiver operating characteristic curve and the accuracy were 0.76 and 0.75, respectively. For prediction of molecular subtype status, proneural subtype shows highest accuracy of 0.93 among all molecular subtypes based on receiver operating characteristic analysis. We find that measures of spatial diversity from point pattern analysis of intensity habitats from T1 post-contrast and T2 fluid-attenuated inversion recovery images are associated with both tumor subtype status and 12-month survival status and may therefore be useful indicators of patient prognosis, in addition to providing potential guidance for molecularly-targeted therapies in Glioblastoma multiforme.

High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management

“…We found that the proportion of higher-order GLCM and RLM features in each feature cluster were rather high (Ki-67: 60%; S-100: 80%; vimentin: 100%; CD34: 100%; high and low grade gliomas: 100%), which indicating that radiomics features were more effective than the traditional image morphology features. Radiomics features contributed more for improving the accuracy of the model, which was consistent with previous research [48][49][50][51][52][53]. The correlation analysis of the total selected 16 features showed low feature redundancy in each model and each feature have provided independent prediction information.…”

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

“…These results, obtained using shape features, are also complementary to previous studies, suggesting a link between phenotype texture and the survival of patients with GBM. 22,[35][36][37] This study is also related to recent work establishing a link between image features, based on spatial habitats 38 and texture (i.e. fractal texture analysis, histogram of oriented gradients, run length, local binary patterns and Haralick features) 39 and the 12-month survival status of patients with GBM.…”

Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma

“…Multiple methods for assessing imaging features and characterizing pixel intensity distributions by quantifying gray levels have been described [ 8 – 11 ]. These methods allow for rigorous and reproducible derivation of detailed, pertinent information, and have been used to analyze MRI features, such as apparent diffusion coefficient, 2-dimensional (2D) spatial habitats [ 12 ], and texture features. These characteristics correlate with the grade of disease, patient survival, response to chemotherapy, and genetic and epigenetic status [ 12 – 18 ].…”

“…These methods allow for rigorous and reproducible derivation of detailed, pertinent information, and have been used to analyze MRI features, such as apparent diffusion coefficient, 2-dimensional (2D) spatial habitats [ 12 ], and texture features. These characteristics correlate with the grade of disease, patient survival, response to chemotherapy, and genetic and epigenetic status [ 12 – 18 ]. With recent advances in radiomics and radiogenomics (or imaging-genomcis), molecular and genetic heterogeneity can be inferred from MRI features by correlating imaging datasets with corresponding molecular and clinical information.…”

“…Several studies have correlated specific features seen in MRI of GBM with patient survival and molecular subtype. However, these investigations have been restricted to 1 or 2 particular MRI sequences and typically considered only features from a single 2D slice [ 12 , 16 , 17 ]. In contrast to these single-sequence, slice-by-slice analyses, radiologists review all acquired MRI sequences in their assessments.…”