Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis

We present an extensive literature review of radiomics-based analysis, with a particular focus on computational modeling, machine learning, and fractal-based analysis in improving differential diagnosis and predicting clinical outcomes. Novel strategies in extracting quantitative features, segmentation methods, and their clinical applications are producing  promising results. Moreover, we provide a detailed summary of the morphometric parameters that have so far been proposed as a means of quantifying imaging characteristics of gliomas. Newly emerging radiomic techniques via machine learning and fractal-based analyses holds considerable potential for improving diagnostic and prognostic accuracy of gliomas.Key points• Radiomic features can be mined through computational analysis to produce quantitative imaging biomarkers that characterize intra-tumoral dynamics throughout the course of treatment.• Surrogate image biomarkers identified through radiomics could enable a non-invasive means of characterizing biologic activities of gliomas.• With novel analytic algorithms, quantification of morphological or sub-regional tumor features to predict survival outcomes is producing promising results.• Quantifying intra-tumoral heterogeneity may improve grading and molecular sub-classifications of gliomas.• Computational fractal-based analysis of gliomas allows geometrical evaluation of tumor irregularities and complexity, leading to novel techniques for tumor segmentation, grad...
Source: Neuroradiology - Category: Radiology Source Type: research