Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas

Abstract

Background

Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging.

Purpose

To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas.

Material and Methods

This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas.

Results

Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of K^trans, skewness of V_e, and K_app−90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05).

Conclusion

Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.

Keywords

Glioma isocitrate dehydrogenase dynamic contrast-enhanced magnetic resonance imaging diffusion kurtosis imaging texture analysis histogram analysis

Get full access to this article

View all access options for this article.

References

Louis

Perry

Reifenberger

, et al. The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016;131:803–820.

Kickingereder

Sahm

Radbruch

, et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep 2015;5:16238.

Smits

van den Bent

. Imaging correlates of adult glioma genotypes. Radiology 2017;284:316–331.

SongTao

Lei

, et al. IDH mutations predict longer survival and response to temozolomide in secondary glioblastoma. Cancer Sci 2012;103:269–273.

Villanueva-Meyer

Wood

Choi

, 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.

Brat

Aldape

Colman

, et al. cIMPACT-NOW update 3: recommended diagnostic criteria for “diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV”. Acta Neuropathol 2018;136:805–810.

Zhou

Vallieres

Bai

, et al. MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 2017;19:862–870.

Zhou

, et al. Combined texture analysis of diffusion-weighted imaging with conventional MRI for non-invasive assessment of IDH1 mutation in anaplastic gliomas. Clin Radiol 2019;74:154–160.

Kassner

Thornhill

. Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol 2010;31:809–816.

10.

Shi

Ren

, et al. Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery. Eur Radiol 2018;28:1748–1755.

11.

Han

, 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.

12.

Brendle

Hempel

Schittenhelm

, et al. Glioma grading and determination of IDH mutation Status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol 2018;28:421–428.

13.

Sun

Zhao

Shi

, et al. Isocitrate dehydrogenase1 mutation reduces the pericyte coverage of microvessels in astrocytic tumours. J Neurooncol 2019;143:187–196.

14.

Roberts

Brasch

, et al. Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade. AJNR Am J Neuroradiol 2000;21:891–899.

15.

Ulyte

Katsaros

Liouta

, et al. Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016;58:1197–1208.

16.

Castellano

Bonilha

, et al. Texture analysis of medical images. Clin Radiol 2004;59:1061–1069.

17.

Kinoshita

Sakai

Arita

, et al. Introduction of high throughput magnetic resonance T2-weighted image texture analysis for WHO grade 2 and 3 gliomas. PLoS One 2016;11:e164268.

18.

Park

Han

Ahn

, et al. Whole-tumor histogram and texture analyses of DTI for evaluation of IDH1-mutation and 1p/19q-codeletion Status in world health organization grade II gliomas. AJNR Am J Neuroradiol 2018;39:693–698.

19.

Raja

Sinha

Saini

, et al. Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas. Neuroradiology 2016;58:1217–1231.

20.

Amano

Woo

Amano

, et al. MRI texture analysis of background parenchymal enhancement of the breast. Biomed Res Int 2017;2017:4845909.

21.

Zhu

, et al. Whole-tumor quantitative apparent diffusion coefficient histogram and texture analysis to differentiation of minimal fat angiomyolipoma from clear cell renal cell carcinoma. Acad Radiol 2019;26:632–639.

22.

Jensen

Helpern

Ramani

, et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432–1440.

23.

Bai

Lin

Tian

, et al. Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology 2016;278:496–504.

24.

Zhao

Wang

, et al. Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status. J Neurooncol 2019;141:195–203.

25.

Wang

, et al. Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: correlation between quantitative parameters and clinical stage. Oncotarget 2017;8:47230–47238.

26.

Wang

Yan

, et al. Histogram analysis of diffusion kurtosis magnetic resonance imaging in differentiation of pathologic Gleason grade of prostate cancer. Urol Oncol 2015;33:315–337.

27.

Winkler

Bell

Zlokovic

. Central nervous system pericytes in health and disease. Nat Neurosci 2011;14:1398–1405.

28.

Zhu

Kang

, et al. Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. Cancer Imaging 2015;15:4.

29.

Zhang

Lyu

, et al. DSC and DCE histogram analyses of glioma biomarkers, including IDH, MGMT, and TERT, on differentiation and survival. Acad Radiol 2020;27:e263–e271

30.

Bralten

Kloosterhof

Balvers

, et al. IDH1 R132h decreases proliferation of glioma cell lines in vitro and in vivo. Ann Neurol 2011;69:455–463.

31.

Jensen

Helpern

. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698–710.

32.

Hempel

Bisdas

Schittenhelm

, et al. In vivo molecular profiling of human glioma using diffusion kurtosis imaging. J Neurooncol 2017;131:93–101.

33.

Xing

Yang

She

, et al. Noninvasive assessment of IDH mutational Status in world health organization grade II and III astrocytomas using DWI and DSC-PWI combined with conventional MR imaging. AJNR Am J Neuroradiol 2017;38:1138–1144.