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
- PMID: 36066259
- DOI: 10.1002/jmri.28419
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
Abstract
Background: Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making.
Purpose: To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements.
Study type: Retrospective.
Population: In all, 109 patients with GBM (n = 57) or SBM (n = 52) were enrolled.
Field strength/sequence: A 3.0 T scanners. T2 -dark-fluid sequence, contrast-enhanced T1 magnetization-prepared rapid gradient echo sequence, and NODDI.
Assessment: ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively.
Statistical tests: Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test.
Results: For the ROI1 and ROI2, the ICVFmin and ODImean obtained the highest area under curve (AUC, AUC = 0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs.
Data conclusion: The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance.
Evidence level: 4 TECHNICAL EFFICACY: Stage 2.
Keywords: glioblastoma multiforme; histogram analysis; neurite orientation dispersion and density imaging; solitary brain metastasis.
© 2022 International Society for Magnetic Resonance in Medicine.
Comment in
-
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".J Magn Reson Imaging. 2023 May;57(5):1475-1476. doi: 10.1002/jmri.28416. Epub 2022 Sep 9. J Magn Reson Imaging. 2023. PMID: 36082991 No abstract available.
References
-
- Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro Oncol 2021;23(8):1231-1251.
-
- Liu S, Shi W, Zhao Q, et al. Progress and prospect in tumor treating fields treatment of glioblastoma. Biomed Pharmacother 2021;141:111810.
-
- Yousefi M, Bahrami T, Salmaninejad A, Nosrati R, Ghaffari P, Ghaffari SH. Lung cancer-associated brain metastasis: Molecular mechanisms and therapeutic options. Cell Oncol (Dordr) 2017;40(5):419-441.
-
- Boire A, Brastianos PK, Garzia L, Valiente M. Brain metastasis. Nat Rev Cancer 2020;20(1):4-11.
-
- Artzi M, Bressler I, Ben BD. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging 2019;50(2):519-528.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical
