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用弥散指标对脑膜瘤进行分级:弥散峰度、平均表观弥散系数、神经纤维方向分散和密度与弥散张量成像的比较。

Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging.

机构信息

Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.

Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, People's Republic of China.

出版信息

Eur Radiol. 2023 May;33(5):3671-3681. doi: 10.1007/s00330-023-09505-3. Epub 2023 Mar 10.

Abstract

OBJECTIVES

To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas.

METHODS

Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13-84 years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed.

RESULTS

The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26-0.34, all p < 0.05).

CONCLUSIONS

Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models.

KEY POINTS

• Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas. • The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status. • DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.

摘要

目的

比较多种扩散指标的直方图特征,以预测脑膜瘤的分级和细胞增殖。

方法

对 122 例脑膜瘤(30 例男性,年龄 13-84 岁)进行扩散谱成像,分为 31 例高级别脑膜瘤(HGMs,2 级和 3 级)和 91 例低级别脑膜瘤(LGMs,1 级)。分析从弥散张量成像(DTI)、弥散峰度成像(DKI)、平均表观传播率(MAP)和神经丝取向分散和密度成像(NODDI)获得的固体肿瘤中多种扩散指标的直方图特征。两组间所有值均采用曼-惠特尼 U 检验进行比较。应用 Logistic 回归分析预测脑膜瘤分级。分析扩散指标与 Ki-67 指数的相关性。

结果

LGMs 中 DKI_AK(轴向峰度)最大值、DKI_AK 范围、MAP_RTPP(返回平面概率)最大值、MAP_RTPP 范围、NODDI_ICVF(细胞内容积分数)范围和 NODDI_ICVF 最大值较低(p<0.0001),而 DTI_MD(平均弥散度)最小值较高(p<0.001)。在 DTI、DKI、MAP、NODDI 和联合扩散模型中,脑膜瘤分级的受试者工作特征曲线下面积(AUCs)无显著差异(AUCs 分别为 0.75、0.75、0.80、0.79 和 0.86;所有校正后 p>0.05,Bonferroni 校正)。Ki-67 指数与 DKI、MAP 和 NODDI 指标呈显著但较弱的正相关(r=0.26-0.34,均 p<0.05)。

结论

来自四个扩散模型的多个扩散指标的全肿瘤直方图分析有望用于脑膜瘤分级。DTI 模型与高级扩散模型具有相似的诊断性能。

关键点

  • 多种扩散模型的全肿瘤直方图分析可用于脑膜瘤分级。

  • DKI、MAP 和 NODDI 指标与 Ki-67 增殖状态呈弱相关。

  • DTI 在脑膜瘤分级方面与 DKI、MAP 和 NODDI 具有相似的诊断性能。

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