Patterns of accentuated grey–white differentiation on diffusion-weighted imaging or the apparent diffusion coefficient maps in comatose survivors after global brain injury

A novel nomogram for early prediction of death in severe neurological disease patients with electroencephalographic periodic discharges

OBJECTIVE

To investigate death-related factors in patients with electroencephalographic (EEG) periodic discharges (PDs) and to construct a model for death prediction.

METHODS

This case-control study enrolled a total of 80 severe neurological disease patients with EEG PDs within 72 h of admission to the neuroscience intensive care unit (NICU). According to modified Rankin scale (mRS) scores half a year after discharge, patients were divided into a survival group (<6 points) and a death group (6 points). Their relevant clinical and biochemical indicators as well as EEG characteristics were retrospectively analyzed. Logistic regression analysis was used to identify the risk factors associated with the death of patients with EEG PDs. A death risk prediction model and an individualized nomogram prediction model were constructed, and the prediction performance and concordance of the models were evaluated.

RESULTS

Multivariate logistic regression analysis showed that the involvement of both gray and white matter in imaging, disappearance of EEG reactivity, occurrence of stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and an interval time of 0.5-4 s were independent risk factors for death. A regression model was established according to the multivariate logistic regression analysis, and the area under the curve of this model was 0.9135. The accuracy of the model was 87.01%, the sensitivity was 87.17%, and the specificity was 89.17%. A nomogram model was constructed, and a concordance index of 0.914 was obtained after internal validation.

CONCLUSION

The regression model based on risk factors has high accuracy in predicting the risk of death of patients with EEG PDs.

SIGNIFICANCE

This model can help clinicians in the early assessment of the prognosis of severe neurological disease patients with EEG PDs.

A histogram-based similarity measure for quantitative magnetic resonance imaging: application in acute mild traumatic brain injury

OBJECTIVES

The most commonly used summary metric in neuroimaging is the mean value, but this pays little attention to the shape of the data distribution and can therefore be insensitive to subtle changes that alter the data distribution.

METHODS

We propose a distributional-based metric called the normalized histogram similarity measure (HSM) for characterization of quantitative images. We applied HSM to quantitative magnetic resonance imaging T1 relaxation data of 44 patients with mild traumatic brain injury and compared with data of 43 age-matched controls.

RESULTS

Significant differences were found between the patients and the controls in 8 gray matter regions using the HSM whereas in only 1 gray matter region based on the mean values.

CONCLUSIONS

Our results show that HSM is more sensitive than the standard mean values in detecting brain tissue changes. Future studies on brain tissue properties using quantitative magnetic resonance imaging should consider the use of HSM to properly capture any tissue changes.

[Triexponential diffusion analysis in invasive ductal carcinoma and fibroadenoma]

To simultaneously obtain information on diffusion and perfusion in breast lesions by diffusion-weighted magnetic resonance imaging (DWI), we analyzed three diffusion components using a triexponential function. Eighteen subjects [10 with invasive ductal carcinoma (IDC), 8 with fibroadenoma] were evaluated using DWI with multiple b-values. We derived perfusion-related diffusion, fast free diffusion, and slow restricted diffusion coefficients (Dp, Df, Ds) calculated from the triexponential function using the DWI data. Moreover, the triexponential analysis was compared with biexponential and monoexponential analyses. Each diffusion coefficient with a triexponential function was correlated to a relative enhancement ratio (RER) using dynamic contrast-enhanced MRI. In triexponential analysis, Dp and Ds in IDC were significantly higher than those for fibroadenoma. There was no correlation between each diffusion coefficient from the triexponential analysis in any of the groups (Dp, Df, and Ds), but biexponential analysis revealed a positive correlation between each diffusion coefficient in breast lesions. Strong correlations were found between Dp and RERs. Triexponential analysis thus makes it possible to obtain, in noninvasive fashion, more detailed diffusion and perfusion information in breast lesions.