脑胶质瘤表观扩散系数与肿瘤分级及IDH1突变的关系

doi:10.3969/j.issn.1006-7795.2021.02.021

摘要/Abstract

摘要： 目的探讨MR扩散加权成像(diffusion weighted imaging, DWI)的表观扩散系数(apparent diffusion coefficient,ADC)在脑胶质瘤分级及分型中的意义,进一步探讨ADC与异柠檬酸脱氢酶-1(isocitrate dehydrogenase-1,IDH1)突变分型的相关性。方法收集首都医科大学宣武医院经病理证实的82例脑胶质瘤患者的影像资料,测量肿瘤实质区、对侧正常白质ADC值,并计算最小ADC值(minimum ADC, ADC_min)、平均ADC值(mean ADC, ADC_mean)、相对最小ADC值(relative minimum ADC,rADC_min)、相对平均ADC值(relative mean ADC,rADC_mean)值,探讨所有ADC值在各级别胶质瘤及亚型间的差异以及其与IDH1突变的关系。结果 82例脑胶质瘤患者中2016世界卫生组织(World Health Organization, WHO)分级Ⅱ级27例、Ⅲ级22例、Ⅳ级33例,其中低级别27例、高级别55例; Ⅱ~Ⅲ级胶质瘤病理分型为星形细胞瘤(astrocytoma, AC)26例及少突胶质细胞瘤(oligodendroglioma, OD)23例。Ⅱ~Ⅲ级胶质瘤IDH1突变型(IDH1 mutant, IDH1^mut)28例、IDH1野生型(IDH1 wild-type, IDH1wild)21例。肿瘤实质ADC_min、ADC_mean、rADC_min、rADC_mean在胶质瘤Ⅱ级与Ⅳ级、Ⅲ级与Ⅳ级之间差异均有统计学意义(P均<0.05),而在Ⅱ级与Ⅲ级之间差异无统计学意义(P>0.05);ADC_min、ADC_mean、rADC_min、rADC_mean在高低级别胶质瘤之间差异有统计学意义(P=0.023、0.029、0.024、0.035);ADC_min、rADC_min、rADC_mean在AC及OD之间差异有统计学意义(P=0.044、0.025、0.040);ADC_min、ADC_mean、rADC_min、rADC_mean在IDH1^mut及IDH1wild之间差异均无统计学意义(P>0.05)。结论术前ADC值有助于胶质瘤的分级及分型,但不能预估IDH1突变状态。

关键词: 胶质瘤, 表观扩散系数, 分级, 异柠檬酸脱氢酶-1

Abstract: Objective To investigate the value of apparent diffusion coefficient (ADC) from diffusion weighted imaging (DWI) in determinate glioma grades and subtype, and investigate the correlation between ADC and isocitrate dehydrogenase-1 (IDH1) mutant.Methods We retrospectively analyzed 82 patients with glioma confirmed by postoperative pathology in our hospital. ADC values of the tumor parenchymal area and contralateral normal white matter were measured, and minimum ADC values, mean ADC values, relative minimum ADC values, relative mean ADC values were calculated. All ADC values in glioma at each grade and subtype were statistically compared. The correlation between all ADC values and IDH1 mutation status were analyzed. Results There were 27 cases,22 cases and 33 cases of World Health Organization (WHO) Ⅱ, Ⅲ and Ⅳ grades glioma respectively, low-grade 27 cases and high-grade 55 cases. The pathology subtype of Ⅱ and Ⅲ grades glioma were astrocytoma and oligodendroglioma, astrocytoma 26 cases and oligodendroglioma 23 cases respectively. There were 28 cases of IDH1^mut and 21 cases of IDH1^wild in all the Ⅱ and Ⅲ grades glioma. Minimum ADC (ADC_min), mean ADC (ADC_mean), relative minimum ADC (rADC_min),relative mean ADC (rADC_mean) between grade Ⅱ glioma group and grade Ⅳ glioma group, grade Ⅲ glioma group and grade Ⅳ glioma group were statistically significant (P<0.05), grade Ⅱ glioma group and grade Ⅲ glioma group were not statistically significant (P>0.05), All ADC values between high and low grade glioma were statistically significant(P=0.023,0.029,0.024,0.035). ADC_min,rADC_min,rADC_mean between astrocytoma and oligodendroglioma were statistically significant(P=0.044,0.025,0.040). All ADC values between IDH1^mut and IDH1^wild were not statistically significant(P>0.05). -Conclusions- ADC values could be used to predict the preoperative pathological grade and subtype of glioma, however those couldn’t be used to predict the preoperative IDH1 mutant.

Key words: glioma, apparent diffusion coefficient, grade, isocitrate dehydrogenase-1

中图分类号:

R445

闫新亭, 宋双双, 卢洁. 脑胶质瘤表观扩散系数与肿瘤分级及IDH1突变的关系[J]. 首都医科大学学报, 2021, 42(2): 286-292.

Yan Xinting, Song Shuangshuang, Lu Jie. Study of apparent diffusion coefficient and glioma grading and molecular IDH1 mutant[J]. Journal of Capital Medical University, 2021, 42(2): 286-292.

参考文献

[1] Chen W, Zheng R, Baade P D, et al.Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132.
[2] Lapointe S, Perry A, Butowski N A.Primary brain tumours in adults[J]. Lancet, 2018, 392(10145):432-446.
[3] Louis D N, Perry A, Reifenberger G, et al.The 2016 World Health Organization classification of tumors of the central nervous system: a summary[J]. Acta Neuropathol, 2016, 131(6):803-820.
[4] Eckel-Passow J E, Lachance D H, Molinaro A M, et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors[J]. N Engl J Med, 2015, 372(26):2499-2508.
[5] Latysheva A, Eeg Emblem K, Server A, et al.Survival associations using perfusion and diffusion magnetic resonance imaging in patients with histologic and genetic defined diffuse glioma World Health Organization Grades II and III[J]. J Comput Assist Tomogr, 2018, 42(5):807-815.
[6] Zhang L, Min Z, Tang M, et al.The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: evidence from a meta-analysis[J]. J Neurol Sci, 2017, 373:9-15.
[7] Hygino da Cruz L C Jr, Vieira I G, Domingues R C. Diffusion MR imaging: an important tool in the assessment of brain tumors[J]. Neuroimaging Clin N Am, 2011, 21(1):27-49.
[8] Murakami R, Hirai T, Sugahara T, et al.Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one-versus two-parameter pilot method[J]. Radiology, 2009, 251(3):838-845.
[9] Xu J, Xu H, Zhang W, et al.Contribution of susceptibility- and diffusion-weighted magnetic resonance imaging for grading gliomas[J]. Exp Ther Med, 2018, 15(6):5113-5118.
[10] Zhang C B, Zhu P, Yang P, et al.Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling[J]. Oncotarget, 2015, 6(34):36643-36651.
[11] Wesseling P, van den Bent M, Perry A. Oligodendroglioma: pathology, molecular mechanisms and markers[J]. Acta Neuropathol, 2015, 129(6):809-827.
[12] Perry A, Wesseling P.Histologic classification of gliomas[J]. Handb Clin Neurol, 2016, 134:71-95.
[13] Latysheva A, Emblem K E, Brandal P, et al.Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach[J]. Neuroradiology, 2019, 61(5):545-555.
[14] 朱莉,耿道颖,任广,等. 胶质瘤最小表观扩散系数值与Ki-67表达的相关性[J].中华医学杂志,2015,95(1):37-39.
[15] 李敏龙,姜萍,罗敏.异柠檬酸脱氢酶-1基因突变与脑胶质瘤关系的研究进展[J].解放军医药杂志,2020,32(3):69-72.
[16] 任瑞,周金亮,房俊芳,等.脑原发性中枢神经系统淋巴瘤与高级别胶质瘤动脉自旋标记联合弥散加权成像鉴别诊断研究[J].中华肿瘤防治杂志,2019,26(17):1274-1279.
[17] 孙梦雪,王雷明,滕梁红.胶质瘤中BRAF基因异常的研究进展[J].首都医科大学学报,2020,41(3):380-384.
[18] 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[J]. AJR Am J Roentgenol, 2018, 210(3):621-628.
[19] 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[J]. Eur Radiol, 2019, 29(2):745-758.
[20] Yamashita K, Hiwatashi A, Togao O, et al.MR imaging-based analysis of glioblastoma multiforme: estimation of IDH1 mutation status[J]. AJNR Am J Neuroradiol, 2016, 37(1):58-65.
[21] Lee S, Choi S H, Ryoo I, et al.Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging[J]. J Neurooncol, 2015, 121(1):141-150.
[22] Feraco P, Bacci A, Ferrazza P, et al.Magnetic resonance imaging derived biomarkers of idh mutation status and overall survival in grade III astrocytomas[J]. Diagnostics (Basel), 2020, 10(4):E247.
[23] Jiao Y, Killela P J, Reitman Z J, et al.Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas[J]. Oncotarget, 2012, 3(7):709-722.
[24] Wu C C, Jain R, Radmanesh A, et al.Predicting genotype and survival in glioma using standard clinical mr imaging apparent diffusion coefficient images: a pilot study from the cancer genome atlas[J]. AJNR Am J Neuroradiol, 2018, 39(10):1814-1820.