基于神经突方向离散度与密度成像的栖息地亚区域分析可增强胶质瘤中异柠檬酸脱氢酶基因分型。

Habitat subregions analysis based on neurite orientation dispersion and density imaging enhances isocitrate dehydrogenase genotyping in glioma.

作者信息

Yang Zheting, Lin Ruolan, Wu Zhenxing, Song Yang, Yang Guang, Jiang Rifeng, Xue Yunjing, Wáng Yì Xiáng J

机构信息

Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, Fujian, 350001, China.

MR Research Collaboration Team, Siemens Healthcare, Shanghai, China.

出版信息

BMC Med Imaging. 2025 Sep 26;25(1):384. doi: 10.1186/s12880-025-01934-4.

DOI:10.1186/s12880-025-01934-4

PMID:41013455

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12465187/

Abstract

BACKGROUND

Noninvasive isocitrate dehydrogenase (IDH) genotyping in gliomas remains a critical challenge. This study investigates the performance of the whole-tumor histogram analysis of neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI) in IDH genotyping and further explores their differences across habitat subregions.

METHODS

This prospective study enrolled participants with suspected gliomas who underwent MRI scans before surgery and calculated diffusion metrics from DTI and NODDI. The whole-tumor region, including tumors and peritumoral edema, was delineated. Otsu’s thresholding method was used to divide the whole-tumor region into Habitat D (DTI-based, Otsu-segmented) based on fractional anisotropy (FA) and mean diffusivity (MD) derived from DTI, and into Habitat N (NODDI-based, Otsu-segmented) based on intracellular volume fraction (ICVF) and orientation dispersion index (ODI) derived from NODDI. Histogram features were extracted from the whole-tumor region and each habitat’s subregions. The Mann-Whitney U test was used to assess the differences in histogram features between different IDH genotypes. Logistic regression models were established to predict IDH genotypes. ROC curve analysis and DeLong tests were employed to evaluate and compare the diagnostic performance.

RESULTS

A total of 75 participants with IDH-wildtype ( = 39) and IDH-mutant ( = 36) glioma were included. In the whole-tumor region, NODDI and DTI showed comparable diagnostic performance in IDH genotyping (AUC = 0.858 and 0.788, respectively; > 0.05). In the habitat subregions, the histogram features in the Habitat N enhance IDH genotyping performance compared to the whole-tumor region, with the NODDI model outperforming the DTI model (AUC = 0.944 and 0.863, respectively; < 0.05). The nomogram integrating age and the optimal NODDI model achieved high diagnostic performance (AUC = 0.962).

CONCLUSIONS

NODDI-based habitat subregions analysis is a promising approach to further enhance the diagnostic performance of DTI and NODDI histogram features in glioma IDH genotyping, and to capitalize on the advantages of NODDI in capturing the heterogeneity of microstructure.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12880-025-01934-4.

摘要

背景

胶质瘤的无创异柠檬酸脱氢酶（IDH）基因分型仍然是一项严峻挑战。本研究调查了神经突方向离散度与密度成像（NODDI）和扩散张量成像（DTI）的全肿瘤直方图分析在IDH基因分型中的表现，并进一步探讨它们在不同瘤周亚区域的差异。

方法

这项前瞻性研究纳入了疑似患有胶质瘤且在手术前接受了MRI扫描的参与者，并从DTI和NODDI中计算扩散指标。划定了包括肿瘤和瘤周水肿在内的全肿瘤区域。采用大津阈值法，根据DTI得出的分数各向异性（FA）和平均扩散率（MD），将全肿瘤区域划分为基于DTI的瘤周亚区域D（大津分割），并根据NODDI得出的细胞内体积分数（ICVF）和方向离散度指数（ODI），将其划分为基于NODDI的瘤周亚区域N（大津分割）。从全肿瘤区域和每个瘤周亚区域提取直方图特征。采用曼-惠特尼U检验评估不同IDH基因型之间直方图特征的差异。建立逻辑回归模型以预测IDH基因型。采用ROC曲线分析和德龙检验来评估和比较诊断性能。

结果

共纳入75例IDH野生型（n = 39）和IDH突变型（n = 36）胶质瘤患者。在全肿瘤区域，NODDI和DTI在IDH基因分型中表现出相当的诊断性能（AUC分别为0.858和0.788；P > 0.05）。在瘤周亚区域，与全肿瘤区域相比，瘤周亚区域N中的直方图特征增强了IDH基因分型性能，NODDI模型优于DTI模型（AUC分别为0.944和0.863；P < 0.05）。整合年龄和最佳NODDI模型的列线图具有较高的诊断性能（AUC = 0.962）。