The analysis of features aims to quantify variations in intensity values and gray levels. The major interest is in texture, as it reveals patterns characterized by brightness, color, slope, size. Selected features are those bringing differentially informative content regarding texture, shape, statistical descriptions, intensity-based measures etc. Usually, segmentation is performed on annotated images by targeting whole tumors, subregions or peritumoral areas (all defined through regions of interest or ROI) to extract features describing distributions of signal intensities and spatial relationships. Radiomics is a multidisciplinary field that engages scientists in processing various interconnected tasks such as tumor segmentation, image preprocessing, feature extraction, model development, testing and validation. However, the increased imaging centrality driving radiomic studies, especially magnetic resonance imaging (MRI) in GBM, has drawn novel interest in computational modeling to improve diagnosis, prognostication and clinical decision support. Generally responsible for growth and invasion, TME is highly variable at the intra-tumor spatial level, and both cellular and molecular interactions have not yet led to causal explanations. In common with other cancers, the tumor microenvironment (TME) plays a complex role in GBM too. Recent advances in understanding the molecular biology of GBM leveraged studies on genetic alterations and genomic profiles, including subclone diversity, via single cell DNA sequencing, and transcriptome profile diversity, via single cell RNA-sequencing. Population-RWD (real-world data) based meta-analyses suggest that 2-, 3- and 5-year survival in GBM patients only partially improved since 2005. It presents a poor prognosis with a median survival of about 15 months for patients who receive standard therapy (Stupp protocol, based on surgical resection and radiotherapy combined with concomitant chemotherapy). GBM is the most common type of malignant primary brain tumor but is still not well understood. The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. Without treatment, GBM can result in death in about 6 months. Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs.
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