Histogram analysis of advanced diffusion-weighted MRI models for evaluating the grade and proliferative activity of meningiomas

Xiaodan Chen; Yichao Zhang; Hui Zheng; Zhitao Wu; Danjie Lin; Ye Li; Sihui Liu; Yizhu Chen; Rufei Zhang; Yang Song; Yunjing Xue; Lin Lin

doi:10.1016/j.acra.2024.10.047

Histogram analysis of advanced diffusion-weighted MRI models for evaluating the grade and proliferative activity of meningiomas

Acad Radiol. 2024 Nov 20:S1076-6332(24)00836-5. doi: 10.1016/j.acra.2024.10.047. Online ahead of print.

Authors

Xiaodan Chen¹, Yichao Zhang², Hui Zheng³, Zhitao Wu⁴, Danjie Lin³, Ye Li², Sihui Liu³, Yizhu Chen³, Rufei Zhang³, Yang Song⁵, Yunjing Xue⁶, Lin Lin⁷

Affiliations

¹ 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.).
² 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.).
³ 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.).
⁴ Department of Radiology, The Second Hospital of Nanping, Nanping 354200, China (Z.W.).
⁵ MR Scientifc Marketing, Healthineers Ltd, Siemens, Shanghai, China (Y.S.).
⁶ 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.); Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors, Fujian Medical University, Fuzhou 350001, China (Y.X., L.L.).
⁷ 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.); Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors, Fujian Medical University, Fuzhou 350001, China (Y.X., L.L.). Electronic address: linlin@fjmu.edu.cn.

PMID: 39572297
DOI: 10.1016/j.acra.2024.10.047

Abstract

Rationale and objectives: 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.

Materials and methods: 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).

Results: 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.

Conclusions: 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.

Keywords: Diffusion-weighted MRI; Ki-67; Meningioma; Neoplasm grading.