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

Fang-Ying Chiu,Cheng-Yu Chen,Nguyen Quoc Khanh Le

doi:10.3390/jcm10092030

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.

Highlights

Glioblastoma multiforme (GBM) is characterized by its rapid growth and infiltration into the surrounding brain parenchyma
Different GBM regions were accurately labeled into four ROIs and joint intensity color-maps on T2-FLAIR: necrosis, solid part, peritumoral tissue, and peritumoral edema
Computational features were extracted using annotated imaging; both color-coded semantic and features were derived from multimodal MR radiomics to analyze GBM texture features to compare tumor characteristics

Summary

Introduction

Glioblastoma multiforme (GBM) is characterized by its rapid growth and infiltration into the surrounding brain parenchyma. In adults, it occurs most often in the cerebral hemispheres, especially in the frontal and temporal lobes. Several imaging modalities can help diagnose and locate the recurrence of brain tumors, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans. Dynamic contrast-enhanced (DCE) CT/MR perfusion provides functional information about the neoangiogenesis of the tumor. Conventional MRI images providing multi-parametric tissue contrast are the most commonly used modality to assess the treatment response compared with other imaging modalities. Brain MRI helps describe the tumor location and morphology for semiautomated contouring and offers ground truth data for machine learning

Methods

Results

Discussion

Conclusion