Histogram Analysis Based on Neurite Orientation Dispersion and Density <scp>MR</scp> Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two <scp>ROI</scp> Placements

Qi, Jinbo; Wang, Peipei; Zhao, Guohua; Gao, Eryuan; Zhao, Kai; Gao, Ankang; Bai, Jie; Zhang, Huiting; Yang, Guang; Zhang, Yong; Cheng, Jingliang

doi:10.1002/jmri.28419

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Advanced diffusion models describe the displacement of the water molecules more accurately, which can illustrate the microstructural information of the tissue better. Several non-Gaussian diffusion models have been used to evaluate glioma, and they performed well in predicting glioma genotyping ( Gao et al, 2022 ) and distinguishing glioblastoma from solitary brain metastasis ( Qi et al, 2022 ; Wang et al, 2022 ). In this study, we evaluated the performance of 4 diffusion models in differentiating glioma with atypical MRI manifestation from brain inflammation, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) models.…”

Section: Introductionmentioning

confidence: 99%

Multiple diffusion metrics in differentiating solid glioma from brain inflammation

Zhao,

Gao,

Gao

et al. 2024

Front. Neurosci.

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Background and purposeThe differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models.Materials and methodsParticipants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated.Results57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758).ConclusionMultiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.

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Section: Introductionmentioning

confidence: 99%

Multiple diffusion metrics in differentiating solid glioma from brain inflammation

Zhao,

Gao,

Gao

et al. 2024

Front. Neurosci.

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“…Currently, VEC, calculated through the ISOVF and ICVF, can be replaced by the ODI. Another study assessed the NODDI histogram analysis for distinguishing between two tumor types and compared the diagnostic performance of placing regions of interest (ROIs) [18]. Traditional diffusion data analysis methods involve pixel/voxel comparisons to identify lesion differences or rely on the mean signal for ROI-based investigations.…”

Section: Introductionmentioning

confidence: 99%

“…Radiomics can acquire quantitative imaging signatures at a high throughput and correlate imaging signatures with targeted clinical outcomes [19][20][21][22][23][24][25]. ROIs and volumes of interest (VOIs) are delineated on the subregions of tumors and lesions [18,26,27]. Thus, radiomics offers diverse imaging information and helps to explore the tumor microenvironment by analyzing well-defined subregional features that more precisely describe tumor heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics

Bai,

He,

Gao

et al. 2024

Eur Radiol

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Objectives To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). Materials and methods In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. Results The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. Conclusion The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. Clinical relevance statement The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. Key Points • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

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“…In this issue of JMRI, an article by Qi et al 6 extends the application of NODDI to the same clinical scenario, using a larger sample size of 109 patients, 57 with GBM and 52 with SBM, all pathologically confirmed. This well‐designed study uses histogram analysis of the enhancing tumor and the peri‐tumoral edema zone separately.…”

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confidence: 99%

Editorial for “Histogram Analysis Based on Neurite Orientation Dispersion and Density Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements”

Abdeen

2022

Magnetic Resonance Imaging

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Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements

Cited by 5 publications

References 40 publications

Multiple diffusion metrics in differentiating solid glioma from brain inflammation

Multiple diffusion metrics in differentiating solid glioma from brain inflammation

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics

Editorial for “Histogram Analysis Based on Neurite Orientation Dispersion and Density Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements”

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