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

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Abstract

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.

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Acknowledgments

Professor Antonio Di Ieva received the 2019 John Mitchell Crouch Fellowship from the Royal Australasian College of Surgeons (RACS) which, along with Macquarie University co-funding, supported the opening of the Computational NeuroSurgery (CNS) Lab at Macquarie University, Sydney, Australia. Moreover, he is supported by an Australian Research Council (ARC) Future Fellowship (2019-2023, FT190100623). KJ would like to personally thank Professor Antonio Di Ieva, Dr. Carlo Russo, and Dr. Abhishta Bhandari for their untiring support during the course of this work. We also thank Dr. Bhandari for contributing to the figure used in this manuscript.

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Authors and Affiliations

  1. Discipline of Surgery, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia

    Kevin Jang

  2. Nepean Hospital, Sydney, New South Wales, Australia

    Kevin Jang

  3. Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia

    Carlo Russo & Antonio Di Ieva

  4. Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Neurosurgery Unit, Macquarie University, Level 2, Suite 201, 2 Technology Place, Sydney, New South Wales, 2109, Australia

    Carlo Russo & Antonio Di Ieva

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  1. Kevin Jang
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Correspondence to Antonio Di Ieva.

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Jang, K., Russo, C. & Di Ieva, A. Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis. Neuroradiology 62, 771–790 (2020). https://doi.org/10.1007/s00234-020-02403-1

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