Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.
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 p...
| Publicado en: | Neuroradiology Vol. 62; no. 7; pp. 771 - 791 |
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| Autores principales: | , , |
| Formato: | diagnostic images review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144203161&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144203161 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2020 vid: 62 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144203161 143949400 144203161 144203161 10.1007/s00234-020-02403-1 144203161 ppf: 771 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis. aug: au: Jang, Kevin Russo, Carlo Di Ieva, Antonio affil: Discipline of Surgery, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia sug: subj: Glioma Radiography Bioinformatics Machine Learning Mathematics Brain Neoplasms ab: 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. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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