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

• Published in: Neuroradiology Vol. 62; no. 7; pp. 771 - 790
• Main Authors: Jang, Kevin, Russo, Carlo, Di Ieva, Antonio
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2020; Springer Nature B.V
• Subjects: Algorithms; Biomarkers; Brain Neoplasms - diagnostic imaging; Computer applications; Diagnosis, Differential; Diagnostic systems; Differential diagnosis; Feature extraction; Fractal analysis; Fractal geometry; Fractal models; Fractals; Glioma; Glioma - diagnostic imaging; Health services; Heterogeneity; Humans; Image acquisition; Image Interpretation, Computer-Assisted - methods; Image segmentation; Imaging; Learning algorithms; Literature reviews; Machine Learning; Medical imaging; Medicine; Medicine & Public Health; Neuroimaging - methods; Neurology; Neuroradiology; Neurosciences; Neurosurgery; Radiology; Radiomics; Review; Temporal variations; Therapeutic applications; Tumors; Glioma; Computational modeling; Fractal analysis; Machine learning; Radiomics
• ISSN: 0028-3940; 1432-1920; 1432-1920
• DOI: 10.1007/s00234-020-02403-1

Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dynamics throughout the course of treatment. This is particularly difficult in gliomas, where heterogeneity has been well established at a molecular level as well as visually in conventional imaging. Thus, acquiring clinically useful features remains difficult due to temporal variations in tumor dynamics. Identifying surrogate biomarkers through radiomics may provide a non-invasive means of characterizing biologic activities of gliomas. 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, grading, and therapeutic monitoring.