Advertisement

DSC and DCE Histogram Analyses of Glioma Biomarkers, Including IDH, MGMT, and TERT, on Differentiation and Survival

  • Author Footnotes
    1 Equal contributors.
    Han-wen Zhang
    Footnotes
    1 Equal contributors.
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Author Footnotes
    1 Equal contributors.
    Gui-wen lyu
    Footnotes
    1 Equal contributors.
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Author Footnotes
    1 Equal contributors.
    Wen-jie He
    Footnotes
    1 Equal contributors.
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Author Footnotes
    2 Equal contributors.
    Yi Lei
    Correspondence
    Address correspondence to: Y.L.
    Footnotes
    2 Equal contributors.
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Author Footnotes
    2 Equal contributors.
    Fan Lin
    Footnotes
    2 Equal contributors.
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Meng-zhu Wang
    Affiliations
    Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, Guangdong Province, China
    Search for articles by this author
  • Hong Zhang
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Li-hong Liang
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Yu-ning Feng
    Affiliations
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
    Search for articles by this author
  • Ji-hu Yang
    Affiliations
    Department of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
    Search for articles by this author
  • Author Footnotes
    1 Equal contributors.
    2 Equal contributors.
Published:January 23, 2020DOI:https://doi.org/10.1016/j.acra.2019.12.010

      Rationale and Objectives

      The World Health Organization 2016 classification of central nervous system tumors added the molecular classification of gliomas and has guiding significance for the operation and prognosis of glioma patients. At present, the perfusion technique plays an important role in judging the malignant degree of glioma. To evaluate the performance of dynamic susceptibility contrast (DSC)- and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) histogram analyses in discriminating the states of molecular biomarkers and survival in glioma patients.

      Materials and Methods

      Forty-three glioma patients who underwent DCE- and DSC-MRI were enrolled. Relevant molecular test results, including those on isocitrate dehydrogenase (IDH), O6-methylguanine-DNA methyltransferase (MGMT) and telomere reverse transcriptase (TERT), were collected. The mean relative cerebral blood volume of DSC-MRI and histogram parameters derived from DCE-MRI (volume transfer coefficient (Ktrans), fractional volume of the extravascular extracellular space (Ve), fractional blood plasma volume (Vp), rate constant between the extravascular extracellular space and blood plasma (Kep) and area under the curve (AUC)) were calculated. Differences in each parameter between gliomas with different expression states (IDH, MGMT, and TERT) were evaluated. The diagnostic efficiency of each parameter was analyzed. The overall survival of all patients was assessed.

      Results

      The 10th percentile AUC (AUC = 0.830, sensitivity = 0.78, specificity = 0.80), the 90th percentile Ve (AUC = 0.816, sensitivity = 0.84, specificity = 0.79), and the mean Kep (AUC = 0.818, sensitivity = 0.76, specificity = 0.78) provided the highest differential efficiency for IDH, MGMT, and TERT, respectively. Kaplan-Meier curves showed a significant difference between subjects with a 10th percentile AUC higher or lower than 0.028 (log-rank = 7.535; p = 0.006) for IDH and between subjects with different 90th percentile Ve values (log-rank = 6.532; p = 0.011) for MGMT.

      Conclusion

      Histogram DCE-MRI demonstrates good diagnostic performance in identifying different molecular types and for the prognostic assessment of glioma.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      REFERENCES

        • Wesseling P.
        • Capper D.
        WHO 2016 classification of gliomas.
        Neuropathol Appl Neurobiol. 2018; 44: 139-150
        • Barresi V.
        • Conti A.
        • Tomasello F.
        Commentary: radiological characteristics and natural history of adult IDH-wild-type astrocytomas with TERT promoter mutations.
        Neurosurgery. 2019; 85: e457-e458
        • Tabouret E.
        • Nguyen A.T.
        • Dehais C.
        • et al.
        Prognostic impact of the 2016 WHO classification of diffuse gliomas in the French POLA cohort.
        Acta Neuropathologica. 2016; 132: 625-634
        • Delgado A.F.
        • Delgado A.F.
        Discrimination between glioma Grades II and III using dynamic susceptibility perfusion MRI: a meta-analysis.
        AJNR Am J Neuroradiol. 2017; 38: 1348-1355
        • Romano A.
        • Pasquini L.
        • Di Napoli A.
        • et al.
        Prediction of survival in patients affected by glioblastoma: histogram analysis of perfusion MRI.
        J Neurooncol. 2018; 139: 455-460
        • Law M.Y.S.
        • Babb J.S.
        Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging.
        AJNR Am J Neuroradiol. 2004; 25: 746-755
        • You S-H.
        • Choi S.H.
        • Kim T.M.
        • et al.
        Differentiation of high-grade from low-grade astrocytoma: improvement in diagnostic accuracy and reliability of pharmacokinetic parameters from DCE MR imaging by using arterial input functions obtained from DSC MR imaging.
        Radiology. 2018; 286: 981-991
        • Lee J.Y.
        • Ahn K.J.
        • Lee Y.S.
        • et al.
        Differentiation of grade II and III oligodendrogliomas from grade II and III astrocytomas: a histogram analysis of perfusion parameters derived from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI.
        Acta Radiologica. 2017; 59: 723-731
        • Lin Y.
        • Xing Z.
        • She D.
        • et al.
        IDH mutant and 1p/19q co-deleted oligodendrogliomas: tumor grade stratification using diffusion-, susceptibility-, and perfusion-weighted MRI.
        Neuroradiology. 2017; 59: 555-562
        • Su C.Q.
        • Lu S.S.
        • Han Q.Y.
        • et al.
        Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading.
        Acta Radiol. 2019; 60: 777-787
        • Hilario A.
        • Hernandez-Lain A.
        • Sepulveda J.M.
        • et al.
        Perfusion MRI grading diffuse gliomas: impact of permeability parameters on molecular biomarkers and survival.
        Neurocirugía (English Edition). 2019; 30: 11-18
        • Smits M.
        • van den Bent M.J.
        Imaging correlates of adult glioma genotypes.
        Radiology. 2017; 284: 316-331
        • Ivanidze J.
        • Lum M.
        • Pisapia D.
        • et al.
        MRI features associated with TERT promoter mutation status in glioblastoma.
        J Neuroimaging. 2019; 29: 357-363
        • Artzi M.
        • Blumenthal D.T.
        • Bokstein F.
        • et al.
        Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma.
        J Neurooncol. 2015; 121: 349-357
        • Huang Y-Q.
        • Liang H-Y.
        • Yang Z-X.
        • et al.
        Value of MR histogram analyses for prediction of microvascular invasion of hepatocellular carcinoma.
        Medicine. 2016; 95
        • Liang H.Y.
        • Huang Y.Q.
        • Yang Z.X.
        • et al.
        Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases.
        Eur Radiol. 2016; 26: 2009-2018
        • Nagasaka K.
        • Satake H.
        • Ishigaki S.
        • et al.
        Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer.
        Breast Cancer. 2019; 26: 113-124
        • Brendle C.
        • Hempel J.M.
        • Schittenhelm J.
        • et al.
        Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion.
        Clin Neuroradiol. 2018; 28: 421-428
        • Villanueva-Meyer J.E.
        • Wood M.D.
        • Choi B.S.
        • et al.
        MRI features and IDH mutational status of Grade II diffuse gliomas: impact on diagnosis and prognosis.
        AJR Am J Roentgenol. 2018; 210: 621-628
        • Suh C.H.
        • Kim H.S.
        • Jung S.C.
        • et al.
        Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis.
        Eur Radiol. 2019; 29: 745-758
        • Yoon R.G.
        • Kim H.S.
        • Paik W.
        • et al.
        Different diagnostic values of imaging parameters to predict pseudoprogression in glioblastoma subgroups stratified by MGMT promoter methylation.
        Eur Radiol. 2017; 27: 255-266
        • Li Z.C.
        • Bai H.
        • Sun Q.
        • et al.
        Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study.
        Eur Radiol. 2018; 28: 3640-3650
        • Zhang Z.Y.
        • Chan K.Y.
        • Ding X.J.
        • et al.
        TERT promoter mutations contribute to IDH mutations in predicting differential responses to adjuvant therapies in WHO grade II and III diffuse gliomas.
        Oncotarget. 2015; 6: 24871-24883
        • Shibahara I.
        • Sonoda Y.
        • Suzuki H.
        • et al.
        Glioblastoma in neurofibromatosis 1 patients without IDH1, BRAF V600E, and TERT promoter mutations.
        Brain Tumor Pathol. 2018; 35: 10-18
        • Xu X.Q.
        • Qian W.
        • Hu H.
        • et al.
        Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging for differentiating malignant from benign orbital lymphproliferative disorders.
        Acta Radiol. 2019; 60: 239-246
        • Louis D.N.
        • Perry A.
        • Reifenberger G.
        • et al.
        The 2016 World Health Organization classification of tumors of the central nervous system: a summary.
        Acta Neuropathol. 2016; 131: 803-820
        • Shu C.
        • Wang Q.
        • Yan X.
        • et al.
        The TERT promoter mutation status and MGMT promoter methylation status, combined with dichotomized MRI-derived and clinical features, predict adult primary glioblastoma survival.
        Cancer Med. 2018; 7: 3704-3712
        • Ballester L.Y.
        • Huse J.T.
        • Tang G.
        • et al.
        Molecular classification of adult diffuse gliomas: conflicting IDH1/IDH2, ATRX, and 1p/19q results.
        Hum Pathol. 2017; 69: 15-22
        • Delfanti R.L.
        • Piccioni D.E.
        • Handwerker J.
        • et al.
        Imaging correlates for the 2016 update on WHO classification of grade II/III gliomas: implications for IDH, 1p/19q and ATRX status.
        J Neurooncol. 2017; 135: 601-609