Chen Xiaodan, Zhang Yichao, Zheng Hui, Wu Zhitao, Lin Danjie, Li Ye, Liu Sihui, Chen Yizhu, Zhang Rufei, Song Yang, Xue Yunjing, Lin Lin
Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China (X.C., Y.Z., H.Z., D.L., Y.L., S.L., Y.C., R.Z., Y.X., L.L.); Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China (X.C.); School of Medical Imaging, Fujian Medical University, Fuzhou 350004, China (X.C., Y.Z., Y.L., Y.X., L.L.).
Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China (X.C., Y.Z., H.Z., D.L., Y.L., S.L., Y.C., R.Z., Y.X., L.L.); School of Medical Imaging, Fujian Medical University, Fuzhou 350004, China (X.C., Y.Z., Y.L., Y.X., L.L.).
Acad Radiol. 2025 Apr;32(4):2171-2181. doi: 10.1016/j.acra.2024.10.047. Epub 2024 Nov 20.
To explore the value of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) magnetic resonance imaging histogram analysis in evaluating the grade and proliferative activity of meningiomas.
A total of 134 meningioma patients were prospectively included and underwent magnetic resonance diffusion imaging. The whole-tumor histogram parameters were extracted from multiple functional maps. Mann-Whitney U test was used to compare the histogram parameters of high- and low-grade meningiomas. The receiver operating characteristic (ROC) curve and multiple logistic regression analysis were used to evaluate the diagnostic efficacy. The correlation between histogram parameters and the Ki-67 index was analyzed. The diffusion model was further validated with an independently validation set (n = 33).
Among single histogram parameters, the variance of NODDI-ISOVF (isotropic volume fraction) showed the highest AUC of 0.829 in grading meningiomas. For the combined models, the DKI model had the best performance in the diagnosis (AUC=0.925). Delong test showed the DKI combined model showed superior diagnostic performance to those of DTI, NODDI and MAP models (P < 0.05 for all). Moreover, moderate to weak correlations were found between various diffusion parameters and the Ki-67 labeling index (rho=0.20-0.45, P < 0.05 for all). In the validation set, the DKI model still showed higher performance (AUC, 0.85) than other diffusion models, thus demonstrating robustness.
Whole-tumor histogram analyses of DTI, DKI, NODDI, and MAP are useful for evaluating the grade and cellular proliferation of meningiomas. DKI combined model has higher diagnostic accuracy than DTI, NODDI and MAP in meningioma grading.
探讨扩散张量成像（DTI）、扩散峰度成像（DKI）、神经突方向离散度与密度成像（NODDI）以及平均表观传播者（MAP）磁共振成像直方图分析在评估脑膜瘤分级和增殖活性方面的价值。
前瞻性纳入134例脑膜瘤患者并进行磁共振扩散成像。从多个功能图中提取全肿瘤直方图参数。采用曼-惠特尼U检验比较高级别和低级别脑膜瘤的直方图参数。使用受试者操作特征（ROC）曲线和多元逻辑回归分析评估诊断效能。分析直方图参数与Ki-67指数之间的相关性。使用独立验证集（n = 33）进一步验证扩散模型。
在单个直方图参数中，NODDI-ISOVF（各向同性体积分数）的方差在脑膜瘤分级中显示出最高的AUC，为0.829。对于联合模型，DKI模型在诊断中表现最佳（AUC = 0.925）。德龙检验显示DKI联合模型的诊断性能优于DTI、NODDI和MAP模型（所有P < 0.05）。此外，发现各种扩散参数与Ki-67标记指数之间存在中度至弱相关性（rho = 0.20 - 0.45，所有P < 0.05）。在验证集中，DKI模型仍显示出比其他扩散模型更高的性能（AUC，0.85），从而证明了其稳健性。
DTI、DKI、NODDI和MAP的全肿瘤直方图分析有助于评估脑膜瘤的分级和细胞增殖。DKI联合模型在脑膜瘤分级中比DTI、NODDI和MAP具有更高的诊断准确性。
