ArticleBMC medical imaging2025
Habitat subregions analysis based on neurite orientation dispersion and density imaging enhances isocitrate dehydrogenase genotyping in glioma.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundNoninvasive 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.
methodsThis 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.
resultsA total of 75 participants with IDH-wildtype (n = 39) and IDH-mutant (n = 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; p > 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; p < 0.05). The nomogram integrating age and the optimal NODDI model achieved high diagnostic performance (AUC = 0.962).
conclusionsNODDI-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.
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