Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Abstract

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test–retest and image registration analyses

“…65,66 On the other hand, there are several variations in the clinical definitions of pseudo-progression based on the imaging reports which require higher precision quantitative imaging. 67 Some radiomics studies have shown the feasibility of MR image radiomic features to discriminate between pseudo-progression compared to true progression [68][69][70] and genomic mutation prediction 8-11,71,72 and treatment response assessments. 5,6 In the present image biomarker discovery era, our results would be important, wherein radiomic features with greatest robustness to image registration between images may be more beneficial in clinical studies.…”

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

“…Radiomics features derived from MRI images have been used to predict grades of gliomas and showed good performance [23,24]. In addition, texture analysis based on MR images has also been applied in molecular or genomic subtyping and their survival outcome relevance for gliomas [25][26][27]. The previous studies for molecular or gene subtyping utilized single-or multi-modal MRI features extracted from sequences of T1WI, T2WI, T2 FLAIR, Contrast-enhanced T1-weighted images (T1-CE), advanced MRI techniques such as diffusion-weighted imaging (DWI) and arterial spin labeling (ASL).…”

Radioresistance in Glioblastoma and the Development of Radiosensitizers

“…Radiomics allows advanced non-invasive assessment of complex imaging features obtained by MRI that may serve as biomarkers [ 46 , 47 ] of disease aggressivity or response. Although these major advances in imaging techniques have substantially improved our ability to diagnose brain tumors, including GBM, overall survival and prognosis for patients with GBM continues to be poor, mostly due to inherent and developed resistance against standard-of-care therapy.…”