Quantitative MRI Measures in SIV-Infected Macaque Brains

Background: Diagnosing Alzheimer’s disease (AD) in its earliest stages is important for therapeutic and support planning. Similarly, being able to predict who will convert from mild cognitive impairment (MCI) to AD would have clinical implications. Objectives: The goals of this study were to identify features from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database associated with the conversion from MCI to AD, and to characterize the temporal evolution of that conversion. Methods: We screened the publically available ADNI longitudinal database for subjects with MCI who have developed AD (cases: n=305), and subjects with MCI who have remained stable (controls: n=250). Analyses included 1,827 features from laboratory assays (n=12), quantitative MRI scans (n=1,423), PET studies (n=136), medical histories (n=72), and neuropsychological tests (n=184). Statistical longitudinal models identified features with significant differences in longitudinal behavior between cases and matched controls. A multiple-comparison adjusted log-rank test identified the capacity of the significant predictive features to predict early conversion. Results: 411 features (22.5%) were found to be statistically different between cases and controls at the time of AD diagnosis; 385 features were statistically different at least 6 months prior to diagnosis, and 28 features distinguished early from late conversion, 20 of which were obtained from neuropsychological tests. In addition, 69 features (3.7%) had statistically significant changes prior to AD diagnosis. Conclusion: Our results characterized features associated with disease progression from MCI to AD, and, in addition, the log-rank test identified features which are associated with the risk of early conversion.

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Routine EEG and Temporal Lobe Epilepsy: Relation to Long-Term EEG Monitoring, Quantitative MRI, and Operative Outcome

Epilepsia ◽

10.1111/j.1528-1157.1996.tb00629.x ◽

1996 ◽

Vol 37 (7) ◽

pp. 651-656 ◽

Cited By ~ 122

Author(s):

Gregory D. Cascino ◽

Max R. Trenerry ◽

Elson L. So ◽

Frank W. Sharbrough ◽

Cheolsu Shin ◽

...

Keyword(s):

Temporal Lobe Epilepsy ◽

Temporal Lobe ◽

Quantitative Mri ◽

Eeg Monitoring ◽

Operative Outcome

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Water capillary absorption in porous media in different wettability conditions studied by quantitative MRI and X-ray CT

Magnetic Resonance Imaging ◽

10.1016/j.mri.2007.01.043 ◽

2007 ◽

Vol 25 (4) ◽

pp. 558-559

Author(s):

M. Gombia ◽

V. Bortolotti ◽

P. Fantazzini ◽

M. Camaiti ◽

T. Schillaci ◽

...

Keyword(s):

Porous Media ◽

Quantitative Mri ◽

Capillary Absorption ◽

X Ray

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Multi‐Site Multi‐Vendor Validation of a Quantitative MRI and CT Compatible Fat Phantom

Medical Physics ◽

10.1002/mp.15038 ◽

2021 ◽

Author(s):

Ruiyang Zhao ◽

Diego Hernando ◽

David T. Harris ◽

Louis A. Hinshaw ◽

Ke Li ◽

...

Keyword(s):

Quantitative Mri

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Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients

npj Precision Oncology ◽

10.1038/s41698-021-00205-z ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Jing Yan ◽

Bin Zhang ◽

Shuaitong Zhang ◽

Jingliang Cheng ◽

Xianzhi Liu ◽

...

Keyword(s):

Image Fusion ◽

Molecular Subtypes ◽

Progression Free Survival ◽

Quantitative Mri ◽

Promoter Mutation ◽

Fusion Model ◽

Free Survival ◽

Contrast Enhanced ◽

Apparent Diffusion ◽

The Status

AbstractGliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.

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Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study

European Radiology ◽

10.1007/s00330-020-07455-8 ◽

2021 ◽

Author(s):

Hugh G. Pemberton ◽

◽

Olivia Goodkin ◽

Ferran Prados ◽

Ravi K. Das ◽

...

Keyword(s):

Visual Assessment ◽

Quantitative Mri ◽

Interrater Agreement ◽

Clinical Image ◽

Single Subject ◽

Volume Loss ◽

Brain Volumes ◽

Additional Information ◽

Clinical Accuracy ◽

Accuracy Study

Abstract Objectives We examined whether providing a quantitative report (QReport) of regional brain volumes improves radiologists’ accuracy and confidence in detecting volume loss, and in differentiating Alzheimer’s disease (AD) and frontotemporal dementia (FTD), compared with visual assessment alone. Methods Our forced-choice multi-rater clinical accuracy study used MRI from 16 AD patients, 14 FTD patients, and 15 healthy controls; age range 52–81. Our QReport was presented to raters with regional grey matter volumes plotted as percentiles against data from a normative population (n = 461). Nine raters with varying radiological experience (3 each: consultants, registrars, ‘non-clinical image analysts’) assessed each case twice (with and without the QReport). Raters were blinded to clinical and demographic information; they classified scans as ‘normal’ or ‘abnormal’ and if ‘abnormal’ as ‘AD’ or ‘FTD’. Results The QReport improved sensitivity for detecting volume loss and AD across all raters combined (p = 0.015* and p = 0.002*, respectively). Only the consultant group’s accuracy increased significantly when using the QReport (p = 0.02*). Overall, raters’ agreement (Cohen’s κ) with the ‘gold standard’ was not significantly affected by the QReport; only the consultant group improved significantly (κs 0.41➔0.55, p = 0.04*). Cronbach’s alpha for interrater agreement improved from 0.886 to 0.925, corresponding to an improvement from ‘good’ to ‘excellent’. Conclusion Our QReport referencing single-subject results to normative data alongside visual assessment improved sensitivity, accuracy, and interrater agreement for detecting volume loss. The QReport was most effective in the consultants, suggesting that experience is needed to fully benefit from the additional information provided by quantitative analyses. Key Points • The use of quantitative report alongside routine visual MRI assessment improves sensitivity and accuracy for detecting volume loss and AD vs visual assessment alone. • Consultant neuroradiologists’ assessment accuracy and agreement (kappa scores) significantly improved with the use of quantitative atrophy reports. • First multi-rater radiological clinical evaluation of visual quantitative MRI atrophy report for use as a diagnostic aid in dementia.

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