Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach.

Fang-Ying Chiu,Yun Yen

doi:10.3390/cancers14061475

Abstract

Simple SummaryGlioblastomas carry a poor prognosis and usually presents with heterogeneous regions in the brain tumor. Multi-parametric MR images can show morphological characteristics. Radiomics features refer to the extraction of a large number of quantitative measurements that describe the geometry, intensity, and texture which were extracted from contrast-enhanced T1-weighted images from anatomical MRI and metabolic features from PET. It also provides a qualitative image interpretation as well as cellular, molecular, and tumor properties. Thus, it derives additional information about the entire tumor volume which is generally of irregular shape and size from routinely evaluated “non-invasive” imaging biomarkers techniques. We demonstrated volumetric habitats and signatures in necrosis, solid tumor, peritumoral tissue, and edema with key biological processes and phenotype features. This provides physicians with key information on how the disease is progressing in the brain and can also give an indication of how well treatment is working.Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system. It encroaches on brain tissue with heterogeneous regions of a necrotic core, solid part, peritumoral tissue, and edema. This study provided qualitative image interpretation in GBM subregions and radiomics features in quantitative usage of image analysis, as well as ratios of these tumor components. The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients with cerebral gliomas. Based on efficiently classified and retrieved cerebral multi-parametric MRI, all data were analyzed to derive volume-based data of the entire tumor from local cohorts and The Cancer Imaging Archive (TCIA) cohorts with GBM. Edema was mainly enriched for homeostasis whereas necrosis was associated with texture features. The proportional volume size of the edema was about 1.5 times larger than the size of the solid part tumor. The volume size of the solid part was approximately 0.7 times in the necrosis area. Therefore, the multi-parametric MRI-based radiomics model reveals efficiently classified tumor subregions of GBM and suggests that prognostic radiomic features from routine MRI examination may also be significantly associated with key biological processes as a practical imaging biomarker.

Highlights

Glioblastoma multiforme (GBM) is the most lethal and aggressive primary brain tumor in adults; with a poor prognosis despite surgical resection combined with radiotherapy and temozolomide administration, the two years survival rate remains at 27% [1]
To investigate which biological processes and morphological characteristics on multiparametric MR images drive volumetric tumor phenotype features in GBM, we performed the qualitative and quantitative interpretation based on semantic features in GBM subregions
Volumetric features were significantly associated with various sets of tumor phenotype features and biological processes in local (Figure 4A,C,E) and The Cancer Imaging Archive (TCIA) (Figure 4B,D,F) cohort studies

Summary

Introduction

Glioblastoma multiforme (GBM) is the most lethal and aggressive primary brain tumor in adults; with a poor prognosis despite surgical resection combined with radiotherapy and temozolomide administration, the two years survival rate remains at 27% [1]. World Health Organization (WHO) began to integrate molecular and genetic profiling to assist in diagnosis [2] and predictive prognosis. The novel technique becomes more popular in data mining, especially in radiomics features. It can approach tumor phenotypes using thousands of image features that result in the basis for cluster shape, pixel intensity histogram, texture, and diffusion kurtosis analysis covering the entire tumor volume [3]. Several explorative studies have shown the high molecular heterogeneity of gliomas such as the isocitrate dehydrogenase

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