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Radiomics for precision medicine in glioblastoma

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Abstract

Introduction

Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients.

Methods

We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.

Results

Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.

Conclusion

Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.

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Data availability

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Code availability

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Authors and Affiliations

  1. Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan

    Kiran Aftab & Syed Ather Enam

  2. Medical College, Aga Khan University, Karachi, Pakistan

    Faiqa Binte Aamir & Saad Mallick

  3. Department of Radiology, Aga Khan University, Karachi, Pakistan

    Fatima Mubarak

  4. Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Whitney B. Pope

  5. Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI, USA

    Tom Mikkelsen

  6. Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA

    Jack P. Rock

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  1. Kiran Aftab
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  2. Faiqa Binte Aamir
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  4. Fatima Mubarak
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  5. Whitney B. Pope
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  8. Syed Ather Enam
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Contributions

KA—conceptualization, drafting, revising, reviewing, supervision, FBA—drafting, revising, SM—drafting, revising, FM—revising, reviewing, supervision, SAE—conceptualization, revising, reviewing, supervision, WBP—revising, reviewing, TM—revising, reviewing, JPR—revising, reviewing.

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Correspondence to Syed Ather Enam.

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Aftab, K., Aamir, F.B., Mallick, S. et al. Radiomics for precision medicine in glioblastoma. J Neurooncol 156, 217–231 (2022). https://doi.org/10.1007/s11060-021-03933-1

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