Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II-III diffuse gliomas.
Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II-III diffuse gliomas.
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DOI：
10.1007/s11060-017-2506-9
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发表时间：
2017-08
影响因子：
3.9
通讯作者：
Ellingson BM
中科院分区：
文献类型：
--
作者：
Leu K;Ott GA;Lai A;Nghiemphu PL;Pope WB;Yong WH;Liau LM;Cloughesy TF;Ellingson BM
在WHO II级和III级弥漫性胶质瘤中，灌注和弥散加权MRI在根据2007年WHO胶质瘤分类方案（即星形细胞瘤与少突胶质细胞瘤）区分组织学亚型和根据2016年WHO重新分类（例如1 p/19 q共缺失和IDH 1突变状态）区分遗传亚型方面的价值仍存在争议。在目前的研究中，我们描述了独特的组织学和遗传性胶质瘤亚型之间的灌注和扩散MR签名。本研究纳入了65例具有2007个组织学名称（星形细胞瘤和少突胶质细胞瘤）、1 p/19 q状态（+ =完整/- =共缺失）和IDH 1突变状态（MUT/WT）的患者。在所有患者中，中位数相对脑血容量（rCBV）和表观扩散系数（ADC）的估计内T2高信号病变。使用Bootstrap假设检验比较按WHO分级和2007或2016胶质瘤分类方案分离的胶质瘤亚群。还使用多变量logistic回归模型来区分1 p19 q+和1 p19 q- WHO II-III胶质瘤。单纯星形细胞瘤和单纯少突胶质细胞瘤的组织学亚型之间的rCBV和ADC均无显著差异。分子亚型间ADC值差异有统计学意义（P = 0.0016），尤其是IDHWT和IDHMUT/1 p19 q+之间差异有统计学意义（P = 0.0013）。IDHMUT/1 p19 q + III级胶质瘤的中位ADC较高; IDHWT III级胶质瘤的rCBV较高，ADC较低; IDHMUT/1 p19 q-的rCBV和ADC值居中，与II级胶质瘤相似。多变量逻辑回归模型能够区分IDHWT和IDHMUT WHO II和III胶质瘤，AUC为0.84（p < 0.0001，灵敏度为74%，特异性为79%）。在IDHMUT WHO II-III胶质瘤中，单独的多变量logistic回归模型能够区分1 p19 q+和1 p19 q- WHO II-III胶质瘤，AUC为0.80（p = 0.0015，灵敏度为64%，特异性为82%）。与2007年WHO指南中基于组织的组织学特征概述的分类方案相比，根据2016年WHO指南，ADC更好地区分了神经胶质瘤的遗传亚型。结果表明，结合rCBV、ADC、T2高信号体积和对比增强的存在，可能有助于非侵入性识别弥漫性胶质瘤的遗传亚型。
The value of perfusion and diffusion-weighted MRI in differentiating histological subtypes according to the 2007 WHO glioma classification scheme (i.e. astrocytoma vs. oligodendroglioma) and genetic subtypes according to the 2016 WHO reclassification (e.g. 1p/19q co-deletion and IDH1 mutation status) in WHO grade II and III diffuse gliomas remains controversial. In the current study, we describe unique perfusion and diffusion MR signatures between histological and genetic glioma subtypes. Sixty-five patients with 2007 histological designations (astrocytomas and oligodendrogliomas), 1p/19q status (+ = intact/- = co-deleted), and IDH1 mutation status (MUT/WT) were included in this study. In all patients, median relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) were estimated within T2 hyperintense lesions. Bootstrap hypothesis testing was used to compare subpopulations of gliomas, separated by WHO grade and 2007 or 2016 glioma classification schemes. A multivariable logistic regression model was also used to differentiate between 1p19q+ and 1p19q- WHO II-III gliomas. Neither rCBV nor ADC differed significantly between histological subtypes of pure astrocytomas and pure oligodendrogliomas. ADC was significantly different between molecular subtypes (P = 0.0016), particularly between IDHWT and IDHMUT/1p19q+ (P = 0.0013). IDHMUT/1p19q+ grade III gliomas had higher median ADC; IDHWT grade III gliomas had higher rCBV with lower ADC; and IDHMUT/1p19q- had intermediate rCBV and ADC values, similar to their grade II counterparts. A multivariable logistic regression model was able to differentiate between IDHWT and IDHMUT WHO II and III gliomas with an AUC of 0.84 (p < 0.0001, 74% sensitivity, 79% specificity). Within IDHMUT WHO II-III gliomas, a separate multivariable logistic regression model was able to differentiate between 1p19q+ and 1p19q- WHO II-III gliomas with an AUC of 0.80 (p = 0.0015, 64% sensitivity, 82% specificity). ADC better differentiated between genetic subtypes of gliomas according to the 2016 WHO guidelines compared to the classification scheme outlined in the 2007 WHO guidelines based on histological features of the tissue. Results suggest a combination of rCBV, ADC, T2 hyperintense volume, and presence of contrast enhancement together may aid in non-invasively identifying genetic subtypes of diffuse gliomas.
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影响因子：
12.7
作者：
Olar A;Wani KM;Alfaro-Munoz KD;Heathcock LE;van Thuijl HF;Gilbert MR;Armstrong TS;Sulman EP;Cahill DP;Vera-Bolanos E;Yuan Y;Reijneveld JC;Ylstra B;Wesseling P;Aldape KD
通讯作者：
Aldape KD
影响因子：
15.9
作者：
Ichimura K;Pearson DM;Kocialkowski S;Bäcklund LM;Chan R;Jones DT;Collins VP
通讯作者：
Collins VP
影响因子：
3.5
作者：
Leu, K.;Boxerman, J. L.;Ellingson, B. M.
通讯作者：
Ellingson, B. M.
影响因子：
4.4
作者：
Leu, Kevin;Boxerman, Jerrold L.;Ellingson, Benjamin M.
通讯作者：
Ellingson, Benjamin M.
影响因子：
6.4
作者：
Felsberg, J;Erkwoh, A;Reifenberger, G
通讯作者：
Reifenberger, G