Author name cluster

Elias R. Melhem

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

YNICL Journal 2015 Journal Article

T1rho MRI and CSF biomarkers in diagnosis of Alzheimer's disease

Mohammad Haris
Santosh K. Yadav
Arshi Rizwan
Anup Singh
Kejia Cai
Deepak Kaura
Ena Wang
Christos Davatzikos

In the current study, we have evaluated the performance of magnetic resonance (MR) T1rho (T1ρ) imaging and CSF biomarkers (T-tau, P-tau and Aβ-42) in characterization of Alzheimer's disease (AD) patients from mild cognitive impairment (MCI) and control subjects. With informed consent, AD (n = 27), MCI (n = 17) and control (n = 17) subjects underwent a standardized clinical assessment and brain MRI on a 1.5-T clinical-scanner. T1ρ images were obtained at four different spin-lock pulse duration (10, 20, 30 and 40 ms). T1ρ maps were generated by pixel-wise fitting of signal intensity as a function of the spin-lock pulse duration. T1ρ values from gray matter (GM) and white matter (WM) of medial temporal lobe were calculated. The binary logistic regression using T1ρ and CSF biomarkers as variables was performed to classify each group. T1ρ was able to predict 77.3% controls and 40.0% MCI while CSF biomarkers predicted 81.8% controls and 46.7% MCI. T1ρ and CSF biomarkers in combination predicted 86.4% controls and 66.7% MCI. When comparing controls with AD, T1ρ predicted 68.2% controls and 73.9% AD, while CSF biomarkers predicted 77.3% controls and 78.3% for AD. Combination of T1ρ and CSF biomarkers improved the prediction rate to 81.8% for controls and 82.6% for AD. Similarly, on comparing MCI with AD, T1ρ predicted 35.3% MCI and 81.9% AD, whereas CSF biomarkers predicted 53.3% MCI and 83.0% AD. Collectively CSF biomarkers and T1ρ were able to predict 59.3% MCI and 84.6% AD. On receiver operating characteristic analysis T1ρ showed higher sensitivity while CSF biomarkers showed greater specificity in delineating MCI and AD from controls. No significant correlation between T1ρ and CSF biomarkers, between T1ρ and age, and between CSF biomarkers and age was observed. The combined use of T1ρ and CSF biomarkers have promise to improve the early and specific diagnosis of AD. Furthermore, disease progression form MCI to AD might be easily tracked using these two parameters in combination.

Details DOI

YNIMG Journal 2009 Journal Article

Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging

Sumei Wang
Sungheon Kim
Sanjeev Chawla
Ronald L. Wolf
Wei-Guo Zhang
Donald M. O'Rourke
Kevin D. Judy
Elias R. Melhem

The purpose of this study is to determine whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as linear and planar anisotropy coefficients (CL and CP) can help differentiate glioblastomas from solitary brain metastases. Sixty-three patients with histopathologic diagnosis of glioblastomas (22 men, 16 women, mean age 58. 4 years) and brain metastases (13 men, 12 women, mean age 56. 3 years) were included in this study. Contrast-enhanced T1-weighted, fluid-attenuated inversion recovery (FLAIR) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CL and CP maps were co-registered and each lesion was semi-automatically subdivided into four regions: central, enhancing, immediate peritumoral and distant peritumoral. DTI metrics as well as the normalized signal intensity from the contrast-enhanced T1-weighted images were measured from each region. Univariate and multivariate logistic regression analyses were employed to determine the best model for classification. The results demonstrated that FA, CL and CP from glioblastomas were significantly higher than those of brain metastases from all segmented regions (p <0. 05), and the differences from the enhancing regions were most significant (p <0. 001). FA and CL from the enhancing region had the highest prediction accuracy when used alone with an area under the curve of 0. 90. The best logistic regression model included three parameters (ADC, FA and CP) from the enhancing part, resulting in 92% sensitivity, 100% specificity and area under the curve of 0. 98. We conclude that DTI metrics, used individually or combined, have a potential as a non-invasive measure to differentiate glioblastomas from metastases.

Details DOI