A systematic review on the current radiogenomics studies in glioblastomas

Sotirios Bisdas, Evangelia Ioannidou, Felice D'Arco

Abstract


Glioblastomas (GBM) have one of the poorest prognoses of any cancer. Current cutting-edge research aims to pave the way for new non-invasive methods of diagnosing brain tumours through innovative imaging techniques and genomic information from tumour samples. Over the past few years, various whole genome sequencing analysis has identified biomarkers and thus gradually changed the way of diagnosing brain tumours. In this context, MRI is a versatile imaging technique as it can provide multifaceted information derived from both morphologic and functional imaging biomarkers (radiomics) in brain. Radiogenomics is attempting to probe any correlation between radiological and histological features and hopefully assess the physiological heterogeneity and genetic alterations paving the way to a holistic approach of the tumour metabolic, pathophysiological and structural fingerprint.  This systematic review aims to summarise the current published evidence of radiogenomics in GBM and also raise awareness for future research in this field.


Keywords


Glioblastoma; Radiomics; Genomics; Biomarkers; Review; MR imaging

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References


Kim Y, Cho H-H, Kim ST, et al. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology 2018; 60(12): 1297-1305.

Daniels LB, Shaya M, Nordberg ML, et al. Glioblastoma multiforme in two non-nuclear family members. J La State Med Soc Off Organ La State Med Soc 2007; 159(4): 215–222.

Urbańska K, Sokołowska J, Szmidt M, et al. Glioblastoma multiforme-an overview. Contemp Oncol 2014; 18(5): 307–312.

Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008; 455(7216): 1061–1068.

Rickert CH, Riemenschneider MJ, Schachenmayr W, et al. Glioblastoma with adipocyte-like tumour cell differentiation-histological and molecular features of a rare differentiation pattern. Brain Pathol Zurich Switz 2009; 19(3): 431–438.

Louis DN, Ohgaki H, Wiestler OD, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol (Berl) 2007; 114(2): 97–109.

Verhaak RGW, Hoadley KA, Purdom E, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterised by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010; 17(1): 98–110.

Malik MT, Kazmi SJ, Turner S. Diagnostic challenges in primary brain stem glioblastoma multiform; a case report. Interdiscip Neurosurg 2017; 10: 104–107.

Nicolaidis S. Biomarkers of glioblastoma multiforme. Metabolism 2015; 64(3, Supplement 1): S22–27.

Ellingson BM, Lai A, Harris RJ, et al. Probabilistic radiographic atlas of glioblastoma phenotypes. AJNR Am J Neuroradiol 2013; 34(3): 533–540.

Zinn PO, Mahajan B, Majadan B, et al. Radiogenomic mapping of oedema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PloS One 2011; 6(10): e25451.

Jamshidi N, Diehn M, Bredel M, et al. Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation. Radiology 2014; 270(1): 1–2.

Gutman DA, Cooper LAD, Hwang SN, et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 2013; 267(2): 560–569.

Colen RR, Vangel M, Wang J, et al. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project. BMC Med Genomics 2014; 7: 30.

Barajas RF, Phillips JJ, Parvataneni R, et al. Regional variation in histopathologic features of tumour specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro-Oncol 2012; 14(7): 942–954.

Hu LS, Ning S, Eschbacher JM, et al. Radiogenomics to characterise regional genetic heterogeneity in glioblastoma. Neuro-Oncol 2017; 19(1): 128–137.

Cho HR, Jeon H, Park CK. Radiogenomics profiling for glioblastoma-related immune cells reveals cd49d expression correlation with MRI parameters and prognosis. Sci Rep 2018; 8(1): 16022.

Qian X, Tan H, Zhang J, et al. Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study. Oncotarget 2016; 7(34): 55377–55394.

Gevaert O, Mitchell LA, Achrol AS, et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 2014; 273(1): 168–174.

Liu X, Mangla R, Tian W, et al. The preliminary radiogenomics association between MR perfusion imaging parameters and genomic biomarkers, and their predictive performance of overall survival in patients with glioblastoma. J Neurooncol 2017; 135(3): 553–560.

Ellingson BM, Abrey LE, Nelson SJ, et al. Validation of postoperative residual contrast-enhancing tumour volume as an independent prognostic factor for overall survival in newly diagnosed glioblastoma. Neuro-Oncol 2018; 20(9): 1240–1250.

Kickingereder P, Bonekamp D, Nowosielski M, et al. Radiogenomics of glioblastoma: Machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology 2016; 281(3): 907–918.

Hong EK, Choi SH, Shin DJ, et al. Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma. Eur Radiol 2018; 28(10): 4350–4361.

Zinn PO, Singh SK, Kotrotsou A, et al. A coclinical radiogenomic validation study: Conserved Magnetic Resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clin Cancer Res 2018; 24(24): 6288-6299.

Demerath T, Simon-Gabriel CP, Kellner E, et al. Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J 2017; 30(1): 36–47.

Smedley NF, Hsu W. Using deep neural networks for radiogenomic analysis. Proc IEEE Int Symp Biomed Imaging 2018; 2018: 1529–1533.




DOI: http://dx.doi.org/10.36162/hjr.v4i3.280

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