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. 2021 Nov 25;13(23):5921.
doi: 10.3390/cancers13235921.

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Anahita Fathi Kazerooni  1   2 , Stephen J Bagley  3   4 , Hamed Akbari  1   2 , Sanjay Saxena  1   2 , Sina Bagheri  2 , Jun Guo  1   2 , Sanjeev Chawla  2 , Ali Nabavizadeh  1   2 , Suyash Mohan  1   2 , Spyridon Bakas  1   2   5 , Christos Davatzikos  1   2 , MacLean P Nasrallah  4   5
Affiliations

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Anahita Fathi Kazerooni et al. Cancers (Basel). .

Abstract

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.

Keywords: GBM; imaging; radiogenomics; radiomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Radiomics pipeline. From left to right: (1) image acquisition; (2) general image pre-processing including image re-orientation, co-registration of the images, and alignment of images with a reference atlas; (3) tumor detection and segmentation; (4) skull stripping and artifact removal (bias field, noise, etc.); (5) feature extraction, such as features of histogram, texture, wavelets, location, morphology, and hemodynamics; (6) predictive modeling using classification or regression; (7) prediction of endpoints, such as patient’s survival [34,35], genomics [11,36,37], response to therapy [38], site of future recurrence [39], or tumor micro-environment [40] (Some graphics are from Servier Medical Art: smart.servier.com (access date: 14 January 2021) and [41]).
Figure 2
Figure 2
The hope: integrating radiomics into the layered diagnosis.
See this image and copyright information in PMC

References

    1. Georgescu M.M., Olar A. Genetic and histologic spatiotemporal evolution of recurrent, multifocal, multicentric and metastatic glioblastoma. Acta Neuropathol. Commun. 2020;8:10. doi: 10.1186/s40478-020-0889-x. - DOI - PMC - PubMed
    1. Lombardi M.Y., Assem M. Glioblastoma genomics: A very complicated story. Exon Publ. 2017:3–25. doi: 10.15586/codon.glioblastoma.2017.ch1. - DOI - PubMed
    1. Ostrom Q.T., Gittleman H., Liao P., Vecchione-Koval T., Wolinsky Y., Kruchko C., Barnholtz-Sloan J.S. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro-oncology. 2017;19:v1–v88. doi: 10.1093/neuonc/nox158. - DOI - PMC - PubMed
    1. Brennan C.W., Verhaak R.G.W., McKenna A., Campos B., Noushmehr H., Salama S.R., Zheng S., Chakravarty D., Sanborn J.Z., Berman S.H., et al. The somatic genomic landscape of glioblastoma. Cell. 2013;155:462. doi: 10.1016/j.cell.2013.09.034. - DOI - PMC - PubMed
    1. Louis D.N., Perry A., Reifenberger G., Von Deimling A., Figarella-Branger D., Cavenee W.K., Ohgaki H., Wiestler O.D., Kleihues P., Ellison D.W. The 2016 World Health Organization classification of tumors of the central nervous system: A summary. Acta Neuropathol. 2016;131:803–820. doi: 10.1007/s00401-016-1545-1. - DOI - PubMed

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