研究动态
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胶质瘤和孤立性脑转移瘤的鉴别:使用直方图分析的多参数磁共振成像研究。

Differentiation of glioma and solitary brain metastasis: a multi-parameter magnetic resonance imaging study using histogram analysis.

发表日期:2024 Jul 05
作者: Yifei Su, Rui Cheng, Jinxia Guo, Miaoqi Zhang, Junhao Wang, Hongming Ji, Chunhong Wang, Liangliang Hao, Yexin He, Cheng Xu
来源: Brain Structure & Function

摘要:

神经胶质瘤和孤立性脑转移瘤(SBM)的鉴别在临床上仍然很复杂,需要活检或多学科诊断。 MR 扩散或分子成像的直方图分析尚未针对分化进行充分研究,并且可能具有改善分化的潜力。总共招募了 65 名新诊断的神经胶质瘤或转移瘤患者。所有患者均接受 DWI、IVIM 和 APTW,以及 T1W、T2W、T2FLAIR 和对比增强 T1W 成像。从肿瘤实质中提取DWI的表观扩散系数(ADC)、慢扩散系数(Dslow)、灌注分数(frac)、IVIM的快扩散系数(Dfast)和APTWI的MTRasym@3.5ppm的直方图特征并进行比较介于神经胶质瘤和 SBM 之间。将具有显着差异的参数通过Logistic回归和接收器算子曲线进行分析,以探索最优模型并比较分化性能。胶质瘤,而 SBM 则观察到更高的 (MTRasym@3.5ppm)10 (P = 0.045)、frac10 (P<0.001)、frac90 (P = 0.001)、fracmean (P<0.001) 和 fracentropy (P<0.001)。脆度(OR = 0.431,95%CI 0.256-0.723,P = 0.002)是SBM分化的独立因素。 (MTRasym@3.5ppm)10、frac10 和压裂峰相结合的模型的 AUC 为 0.857(灵敏度:0.857,特异度:0.750),而 frac10 和压裂峰相结合的模型的 AUC 为 0.824(灵敏度:0.952,特异度:0.750)。 0.591)。两个模型的 AUC 之间没有统计学上的显着差异。 (Z = -1.14, P = 0.25)。增强肿瘤区域的 frac10 和 frackurtosis 可用于区分胶质瘤和 SBM,(MTRasym@3.5ppm)10 有助于提高分化特异性。© 2024。作者。
Differentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn't been fully investigated for the differentiation and may have the potential to improve it.A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance.Higher ADCkurtosis (P = 0.022), frackurtosis (P<0.001),and fracskewness (P<0.001) were found for glioma, while higher (MTRasym@3.5ppm)10 (P = 0.045), frac10 (P<0.001),frac90 (P = 0.001), fracmean (P<0.001), and fracentropy (P<0.001) were observed for SBM. frackurtosis (OR = 0.431, 95%CI 0.256-0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)10, frac10, and frackurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac10 and frackurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25).The frac10 and frackurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)10 helps improving the differentiation specificity.© 2024. The Author(s).