A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning

Abstract

Glioblastoma multiforme (GBM) carries a poor prognosis and usually presents with heterogenous regions of a necrotic core, solid part, peritumoral tissue, and peritumoral edema. Accurate demarcation on magnetic resonance imaging (MRI) between the active tumor region and perifocal edematous extension is essential for planning stereotactic biopsy, GBM resection, and radiotherapy. We established a set of radiomics features to efficiently classify patients with GBM and retrieved cerebral multiparametric MRI, including contrast-enhanced T1-weighted (T1-CE), T2-weighted, T2-weighted fluid-attenuated inversion recovery, and apparent diffusion coefficient images from local patients with GBM. A total of 1316 features on the raw MR images were selected for each annotated area. A leave-one-out cross-validation was performed on the whole dataset, the different machine learning and deep learning techniques tested; random forest achieved the best performance (average accuracy: 93.6% necrosis, 90.4% solid part, 95.8% peritumoral tissue, and 90.4% peritumoral edema). The features from the enhancing tumor and the tumor shape elongation of peritumoral edema region for high-risk groups from T1-CE. The multiparametric MRI-based radiomics model showed the efficient classification of tumor subregions of GBM and suggests that prognostic radiomic features from a routine MRI exam may also be significantly associated with key biological processes that affect the response to chemotherapy in GBM.

Keywords: MRI; glioblastoma; ground truth; machine learning; oncologic imaging; precision medicine; quantitative imaging; radiomics; texture analysis.

Figure 1

A multiparametric MRI-based radiomic analysis in step (1) medical imaging acquisition, (2) imaging segmentation, (3) feature extraction, (4) statistical analysis, and (5) results. The tumor ROI on all MR slices to extract the radiomic features. Features such as tumor shape, histogram, and texture features were extracted from the ROI to discriminate the biological processes of GBM habitats and facilitate personalized precision medicine. Note: GBM, glioblastoma multiforme; ROI, region of interest.

Figure 2

Tumor image signatures and habitats of a 61-year-old woman with left temporoparietal GBM. Illustration of ground truth and semantic features. (A) T2-FLAIR. (B) Red: necrosis, orange: solid part, yellow: peritumoral tissue, green: peritumoral edema.

Figure 3

Illustration of ground truth, semantic features, and histogram on the raw MR images. (A) T1-CE annotation. (B) T2-FLAIR annotation.

Figure 4

Radiomic signature shows representative T1-CE, T2-WI, ADC and T2-FLAIR images that demonstrate tumor habitats color-coded and overlaid, i.e., necrosis (red), solid part (orange), peritumoral tissue (yellow), and peritumoral edema (green). Each annotated area based on the raw MR images. Note: T1-CE, contrast-enhanced T1-weighted; T2-WI, T2-weighted images; T2-FLAIR, T2-weight fluid-attenuated in-version recovery; ADC, apparent diffusion coefficient images.

Figure 5

AUCs of classification of GBM regions using different machine learning/deep learning approaches: (A) k-Nearest Neighbors, (B) Naïve Bayes, (C) Random Forest, and (D) Deep MLP Neural Network.

Figure 6

Correlation heatmap of 20 important radiomics features generated by random forest. (A) Pearson correlation test, (B) Spearman correlation test. The highly correlated features are LBP_Uniformity, surface to volume ratio, spherical disproportion, and LBP_Skewness.