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20.02.2020 | Magnetic Resonance

Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas

verfasst von: Hao Wu, Haipeng Tong, Xuesong Du, Hong Guo, Qiang Ma, Yulong Zhang, Xiaoyue Zhou, Heng Liu, Sunan Wang, Jingqin Fang, Weiguo Zhang

Erschienen in: European Radiology | Ausgabe 6/2020

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Abstract

Objective

The current study aimed to evaluate the clinical practice for hemodynamic tissue signature (HTS) method in IDH genotype prediction in three groups derived from high-grade gliomas.

Methods

Preoperative MRI examinations of 44 patients with known grade and IDH genotype were assigned into three study groups: glioblastoma multiforme, grade III, and high-grade gliomas. Perfusion parameters were analyzed and were used to automatically draw the four reproducible habitats (high-angiogenic enhancing tumor habitats, low-angiogenic enhancing tumor habitats, infiltrated peripheral edema habitats, vasogenic peripheral edema habitats) related to vascular heterogeneity. These four habitats were then compared between inter-patient with IDH mutation and their wild-type counterparts at these three groups, respectively. The discriminating potential for HTS in assessing IDH mutation status prediction was assessed by ROC curves.

Results

Compared with IDH wild type, IDH mutation had significantly decreased relative cerebral blood volume (rCBV) at the high-angiogenic enhancing tumor habitats and low-angiogenic enhancing tumor habitats. ROC analysis revealed that the rCBVs in habitats had great ability to discriminate IDH mutation from their wild type in all groups. In addition, the Kaplan-Meier survival analysis yielded significant differences for the survival times observed from the populations dichotomized by low (< 4.31) and high (> 4.31) rCBV in the low-angiogenic enhancing tumor habitat.

Conclusions

The HTS method has been proven to have high prediction capabilities for IDH mutation status in high-grade glioma patients, providing a set of quantifiable habitats associated with tumor vascular heterogeneity.

Key Points

• The HTS method has a high accuracy for molecular stratification prediction for all subsets of HGG.

• The HTS method can give IDH mutation–related hemodynamic information of tumor-infiltrated and vasogenic edema.

• IDH-relevant rCBV difference in habitats will be a great prognosis factor in HGG.

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Ostrom QT, Gittleman H, Stetson L, Virk SM, Barnholtz-Sloan JS (2015) Epidemiology of gliomas. Cancer Treat Res 163:1–14PubMedCrossRef

Ostrom QT, Gittleman H, Liao P et al (2017) CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro Oncol 19:v1–v88PubMedPubMedCentralCrossRef

Ostrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS (2018) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011-2015. Neuro Oncol 20:iv1–iv86PubMedPubMedCentralCrossRef

Linkous AG, Yazlovitskaya EM (2011) Angiogenesis in glioblastoma multiforme: navigating the maze. Anticancer Agents Med Chem 11:712–718PubMedCrossRef

De Bonis P, Lofrese G, Anile C, Pompucci A, Vigo V, Mangiola A (2013) Radioimmunotherapy for high-grade glioma. Immunotherapy 5:647–659PubMedCrossRef

Rohle D, Popovici-Muller J, Palaskas N et al (2013) An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells. Science 340:626–630PubMedPubMedCentralCrossRef

Gomes DNS, Cucolicchio IC, Mattos MD et al (2017) Is there a correlation between overall survival and the mutation profile of IDH1 and TERT in high grade glioma? A Brazilian experience results. Int J Radiat Oncol 99:E76–E76CrossRef

Parsons DW, Jones S, Zhang X et al (2008) An integrated genomic analysis of human glioblastoma multiforme. Science 321:1807–1812PubMedPubMedCentralCrossRef

Price SJ, Boonzaier NR, Lupson V, Larkin T (2014) Idh-1 mutated glioblastomas have a less invasive phenotype than Idh-1 wild type glioblastomas: a diffusion tensor imaging study. Neuro Oncol 16

10.

Hartmann C, Hentschel B, Wick W et al (2010) Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol 120:707–718PubMedCrossRef

11.

Deng L, Xiong P, Luo Y et al (2018) Association between IDH1/2 mutations and brain glioma grade. Oncol Lett 16:5405–5409PubMedPubMedCentral

12.

Lai A, Kharbanda S, Pope WB et al (2011) Evidence for sequenced molecular evolution of IDH1 mutant glioblastoma from a distinct cell of origin. J Clin Oncol 29:4482–4490PubMedPubMedCentralCrossRef

13.

Dang L, White DW, Gross S et al (2010) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 465:966–966PubMedPubMedCentralCrossRef

14.

Xu W, Yang H, Liu Y et al (2011) Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of alpha-ketoglutarate-dependent dioxygenases. Cancer Cell 19:17–30PubMedPubMedCentralCrossRef

15.

Flavahan WA, Drier Y, Liau BB et al (2016) Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529:110PubMedCrossRef

16.

Dang L, White DW, Gross S et al (2009) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462:739–744PubMedPubMedCentralCrossRef

17.

Alves TR, Lima FR, Kahn SA et al (2011) Glioblastoma cells: a heterogeneous and fatal tumor interacting with the parenchyma. Life Sci 89:532–539PubMedCrossRef

18.

Popov S, Jury A, Laxton R et al (2013) IDH1-associated primary glioblastoma in young adults displays differential patterns of tumour and vascular morphology. Plos One 8PubMedPubMedCentralCrossRef

19.

Pirozzi CJ, Yan H (2018) Improved grading of IDH-mutated astrocytic gliomas. Nat Rev Neurol 14:383–384PubMedCrossRef

20.

Zhang L, He LQ, Lugano R et al (2018) IDH mutation status is associated with distinct vascular gene expression signatures in lower-grade gliomas. Neuro Oncol 20:1505–1516PubMedPubMedCentralCrossRef

21.

Ellingson BM (2015) Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep 15

22.

Chow D, Chang P, Weinberg BD, Bota DA, Grinband J, Filippi CG (2018) Imaging genetic heterogeneity in glioblastoma and other glial tumors: review of current methods and future directions. AJR Am J Roentgenol 210:30–38PubMedCrossRef

23.

Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269:8–15PubMedPubMedCentralCrossRef

24.

Swanton C (2012) Intratumor heterogeneity: evolution through space and time. Cancer Res 72:4875–4882PubMedPubMedCentralCrossRef

25.

Fuster-Garcia E, Juan-Albarracin J, Garcia-Ferrando GA, Marti-Bonmati L, Aparici-Robles F, Garcia-Gomez JM (2018) Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures. NMR Biomed:31

26.

Juan-Albarracin J, Fuster-Garcia E, Perez-Girbes A et al (2018) Glioblastoma: vascular habitats detected at preoperative dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging predict survival. Radiology 287:944–954PubMedCrossRef

27.

Welker K, Boxerman J, Kalnin A et al (2015) ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. AJNR Am J Neuroradiol 36:E41–E51PubMedPubMedCentralCrossRef

28.

Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27:859–867PubMed

29.

Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG (2003) Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 50:164–174PubMedCrossRef

30.

Juan-Albarracin J, Fuster-Garcia E, Manjon JV et al (2015) Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 10:e0125143PubMedPubMedCentralCrossRef

31.

Oei MTH, Meijer FJA, Mordang JJ et al (2018) Observer variability of reference tissue selection for relative cerebral blood volume measurements in glioma patients. Eur Radiol 28:3902–3911PubMedPubMedCentralCrossRef

32.

Qi ST, Yu L, Li HZ et al (2014) Isocitrate dehydrogenase mutation is associated with tumor location and magnetic resonance imaging characteristics in astrocytic neoplasms. Oncol Lett 7:1895–1902PubMedPubMedCentralCrossRef

33.

Jalbert LE, Elkhaled A, Phillips JJ et al (2017) Metabolic profiling of IDH mutation and malignant progression in infiltrating glioma. Sci Rep 7

34.

Su CL, Jiang JJ, Zhang S et al (2019) Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour. Eur Radiol 29:1986–1996PubMedCrossRef

35.

Zhou M, Hall L, Goldgof D et al (2014) Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol 7:5–13PubMedPubMedCentralCrossRef

36.

Caulo M, Panara V, Tortora D et al (2014) Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 272:494–503PubMedCrossRef

37.

Schoenegger K, Oberndorfer S, Wuschitz B et al (2009) Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma? Eur J Neurol 16:874–878PubMedCrossRef

38.

Pope WB, Sayre J, Perlina A, Villablanca JP, Mischel PS, Cloughesy TF (2005) MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am J Neuroradiol 26:2466–2474PubMed

39.

Lemee JM, Clavreul A, Menei P (2015) Intratumoral heterogeneity in glioblastoma: don’t forget the peritumoral brain zone. Neuro Oncol 17:1322–1332PubMedPubMedCentralCrossRef

40.

Das D, Yoon B, Golden L et al (2017) NIMG-37. Correlation of VASARI-based MRI phenotypes with MGMT and IDH status across glioma grades: a statistical analysis in 372 patients. Neuro Oncol 19:150–150PubMedCentralCrossRef

41.

Artzi M, Bokstein F, Blumenthal DT et al (2014) Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastoma during bevacizumab therapy: a longitudinal MRI study. Eur J Radiol 83:1250–1256PubMedCrossRef

42.

Min ZG, Niu C, Rana N, Ji HM, Zhang M (2013) Differentiation of pure vasogenic edema and tumor-infiltrated edema in patients with peritumoral edema by analyzing the relationship of axial and radial diffusivities on 3.0T MRI. Clin Neurol Neurosurg 115:1366–1370PubMedCrossRef

43.

Patel SH, Poisson LM, Brat DJ et al (2017) T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project. Clin Cancer Res 23:6078–6085PubMedCrossRef

44.

Wu S, Meng J, Yu Q, Li P, Fu S (2019) Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas. J Cancer Res Clin Oncol 145:543–550PubMedPubMedCentralCrossRef

45.

Lu CF, Hsu FT, Hsieh KL et al (2018) Machine learning-based radiomics for molecular subtyping of gliomas. Clin Cancer Res 24:4429–4436PubMedCrossRef

46.

Arita H, Kinoshita M, Kawaguchi A et al (2018) Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas. Sci Rep:8

47.

Hsieh KLC, Chen CY, Lo CM (2017) Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas. Oncotarget 8:45888–45897PubMedPubMedCentralCrossRef

48.

Levner I, Drabycz S, Roldan G, De Robles P, Cairncross JG, Mitchell R (2009) Predicting MGMT methylation status of glioblastomas from MRI texture. Med Image Comput Comput Assist Interv 12:522–530PubMed

49.

Zhang X, Tian Q, Wang L et al (2018) Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging 48:916–926PubMedCrossRef

50.

Chang K, Bai HX, Zhou H et al (2018) Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging. Clin Cancer Res 24:1073–1081PubMedCrossRef

51.

Carrillo JA, Lai A, Nghiemphu PL et al (2012) Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR Am J Neuroradiol 33:1349–1355CrossRefPubMed

52.

Smits M, van den Bent MJ (2017) Imaging correlates of adult glioma genotypes. Radiology 284:316–331PubMedCrossRef

53.

Law M, Young RJ, Babb JS et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247:490–498PubMedPubMedCentralCrossRef

54.

Hirai T, Murakami R, Nakamura H et al (2008) Prognostic value of perfusion MR imaging of high-grade astrocytomas: long-term follow-up study. AJNR Am J Neuroradiol 29:1505–1510PubMedCrossRef

55.

Jain R, Poisson L, Narang J et al (2013) Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 267:212–220PubMedPubMedCentralCrossRef

56.

Hartmann C, Hentschel B, Simon M et al (2013) Long-term survival in primary glioblastoma with versus without isocitrate dehydrogenase mutations. Clin Cancer Res 19:5146–5157PubMedCrossRef

57.

Han K, Ren M, Wick W et al (2014) Progression-free survival as a surrogate endpoint for overall survival in glioblastoma: a literature-based meta-analysis from 91 trials. Neuro Oncol 16:696–706PubMedCrossRef

Titel: Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas
verfasst von: Hao Wu
Haipeng Tong
Xuesong Du
Hong Guo
Qiang Ma
Yulong Zhang
Xiaoyue Zhou
Heng Liu
Sunan Wang
Jingqin Fang
Weiguo Zhang
Publikationsdatum: 20.02.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 6/2020
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06702-2

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Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas