Differentiation between glioblastoma and solitary brain metastasis using neurite orientation dispersion and density imaging

Kadota, Yoshihito; Hirai, Toshinori; Azuma, Minako; Hattori, Yohei; Khant, Zaw Aung; Hori, Masaaki; Saito, Kiyotaka; Yokogami, Kiyotaka; Takeshima, Hideo

doi:10.1016/j.neurad.2018.10.005

Cited by 31 publications

(31 citation statements)

References 21 publications

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“…The added value of along-tract NODDI-metrics was clear also when compared to MD, as MD was less sensitive than NODDImetrics overall, and showed a generalized tendency to increase supported by recent findings from other studies (Kadota et al, 2020).…”

Section: Patients' Cohortsupporting

confidence: 74%

“…In fact, while the increase of extracellular volume was frequently found in gliomas and may be suggestive of peritumoral tissue infiltration from cancer cells (Patterns A and B), the circumscribed increase of free fluid is only detected in few patients and may be interpreted as indicative of a vasogenic edema component, as it is particularly evident in the single case of brain metastasis; this hypothesis is supported by recent findings from other studies (Kadota et al, 2020).…”

Section: Patients' Cohortsupporting

confidence: 74%

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Along‐tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors

Pieri

Sanvito

Riva

et al. 2020

Human Brain Mapping

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Along‐tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities, new parameters reflecting the relative contribution of different diffusion compartments in the tissue can be estimated through advanced diffusion MRI methods as neurite orientation dispersion and density imaging (NODDI), leading to a more specific microstructural characterization. In this study, we extracted both DTI‐ and NODDI‐derived quantitative microstructural diffusion metrics along the most eloquent fiber tracts in 15 healthy subjects and in 22 patients with brain tumors. We obtained a robust intraprotocol reference database of normative along‐tract microstructural metrics, and their corresponding plots, from healthy fiber tracts. Each diffusion metric of individual patient's fiber tract was then plotted and statistically compared to the normative profile of the corresponding metric from the healthy fiber tracts. NODDI‐derived metrics appeared to account for the pathological microstructural changes of the peritumoral tissue more accurately than DTI‐derived ones. This approach may be useful for future studies that may compare healthy subjects to patients diagnosed with other pathological conditions.

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Section: Patients' Cohortsupporting

confidence: 74%

Section: Patients' Cohortsupporting

confidence: 74%

Along‐tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors

Pieri

Sanvito

Riva

et al. 2020

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Thus, conventional imaging alone may be insufficient for accurate classification. Prior studies using advanced MRI imaging techniques such as perfusion imaging 10 – 12 , spectroscopy, diffusion-weighted and tensor imaging 13 , new diffusion weighted techniques like neurite orientation dispersion and density imaging 14 , and more recently other advanced sequences like non-contrast inflow-based vascular-space-occupancy MR imaging 15 have been used to distinguish amongst these entities with variable success 16 – 20 . However, these advanced imaging sequences are not performed universally, and conventional imaging is still the mainstay in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics

Priya

Liu

Ward

et al. 2021

Sci Rep

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Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.

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“…NODDI metrics have been identified as useful diagnostic biomarkers for revealing microstructural changes in the brains of patients with Alzheimer's disease [10], Parkinson's disease [7,11,12], stroke [13], and multiple sclerosis [14,15]. Recently, NODDI has been used to differentiate brain tumors [16] and explore white matter (WM) microstructure in very preterm-born children [17]. In addition, NODDI has been used to demonstrate neurite properties in the human cerebral cortex that are correlated with the myeloarchitecture [18].…”

Section: Introductionmentioning

confidence: 99%

Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

et al. 2019

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Purpose The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scanrescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23-37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm 2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan-rescan and inter-vendor reproducibilities of NODDI metrics. Results The scan-rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3-14%) were lower than the scan-rescan reproducibility (CoV: scanner A = 0.8-3.8%; scanner B = 0.8-2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.

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Differentiation between glioblastoma and solitary brain metastasis using neurite orientation dispersion and density imaging

Cited by 31 publications

References 21 publications

Along‐tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors

Along‐tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors

Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics

Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

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