Interscanner Variation in Brain MR Lesion Load Measurements in MS Using Conventional Spin-Echo, RARE and Fast FLAIR Sequences.

BACKGROUND AND PURPOSE: Different MR pulse sequences have been proposed for measuring multiple sclerosis (MS)-related abnormalities. The reproducibility of measured brain MS lesion volumes was compared for MR images performed using different scanners and different pulse sequences. METHODS: Nine patients with relapsing-remitting MS were each imaged on two scanners and, on each occasion, dual-echo conventional spin-echo, dual-echo rapid-acquisition relaxation-enhanced (RARE), and fast fluid-attenuated inversion recovery (fast-FLAIR) images were obtained. The lesion volume present on each image was evaluated three times by a single observer in random order, using a local thresholding technique. RESULTS: The mean lesion volumes present on fast-FLAIR images were significantly higher than those measured on dual-echo conventional spin-echo and RARE images. The mean intraobserver coefficients of variation for the different sequences and scanners ranged from 3.0% to 4.2% (no statistically significant difference). For each of the sequences, the use of different scanners introduced a variability that was higher than the intraobserver variability: the interscanner coefficient of variation was 7.4% for conventional spin-echo, 9.5% for RARE, and 18.5% for fast-FLAIR images. CONCLUSION: Our study confirms that the use of different scanners significantly influences lesion loads measured from MR images of patients with MS and establishes that newer sequences are more susceptible to measurement variability. It also indicates that, if newer sequences are to be used in clinical trials, careful standardization is needed

A diffusion tensor MRI study of patients with MCI and AD with a 2-year clinical follow-up

Objective The authors assessed whether brain changes detected by diffusion tensor (DT) MRI can improve the understanding of structural damage in Alzheimer's disease (AD) and are associated with different risks of conversion to AD in amnestic mild cognitive impairment (aMCI). Methods Twenty-one aMCI patients, 21 AD patients and 20 healthy subjects underwent conventional and DT MRI at baseline. All subjects were clinically followed up over 2 years; at the end of follow-up, aMCI were grouped into converters to AD (aMCI-C) and non-converters (aMCI-NC). The mean diffusivity (MD) and fractional anisotropy (FA) were obtained from total grey matter (GM) and white matter (WM), and from several GM and WM regions of interest (ROIs). On T1-weighted images, normalised volumes of the whole brain (NBV), GM (NGMV) and WM were measured. Results A significant ‘trend’ of worsening with a trajectory ‘normal/aMCI/AD’ was found for NBV and NGMV, total GM and WM MD, total WM FA, as well as for diffusivity abnormalities in several GM and WM ROIs, mainly located in posterior brain regions. aMCI-C had GM and WM changes similar to those seen in AD, whereas aMCI-NC showed a DT MRI pattern similar to that of healthy subjects. DT MRI metrics that better distinguished aMCI-C from aMCI-NC were MD of total GM and WM, hippocampi, anterior insulae, frontal and parietal WM, occipital GM and WM, and FA of temporal WM. Volumetric variables were not able to distinguish the two aMCI subgroups (aMCI-C and aMCI-NC). Conclusions Subtle brain diffusivity changes occur from the prodromal stages of AD, mainly in posterior brain regions, and spread over the course of the disease to involve the frontal lobe. In aMCI, the severity of microstructural damage within and beyond the medial temporal lobe is associated with an increased short-term risk to develop AD

Interferon Beta treatment for multiple sclerosis has a graduated effect on MRI enhancing lesions according to their size and pathology

Objective—The ability of recombinant human interferon â-1a (rh-IFN â-1a) to suppress multiple sclerosis activity, evaluated from MRI, was assessed across a range of lesions enhancing at different gadolinium-DTPA (Gd) doses and with different sizes. Methods—Every 4 weeks, standard dose (Sd; 0.1 mmol/kg Gd) and triple dose (Td; 0.3 mmol/kgGd) MRI were obtained from 18 patients with relapsing-remitting multiple sclerosis for 3 months before and 4 months after starting treatment with 44 μg rh-IFN â-1a subcutaneously, once a week. Results—The total numbers of enhancing lesions were 145 and 126 on Sd scans and 278 and 192 on the Td scans obtained before and after treatment. The introduction of treatment decreased, on average, the rate of appearance of new enhancing lesions seen on Sd and Td scans by 37% (p<0.001). Treatment effects on new enhancing lesions seen on Td scans was, on average, 28% higher than on those seen on Sd scans. The distribution of lesion sizes on Td scans changed significantly during the treatment period (p=0.05), due to a marked decrease in the number of small lesions. Conclusions— The effect of 44 μg rh-IFN â-1a in reducing multiple sclerosis disease activity, as monitored by Gd enhanced MRI, is not homogeneous, but graduated according to the pathological characteristics and size of the lesions

Incorporating Domain Knowledge into the Fuzzy Connectedness Framework: Application to Brain Lesion Volume Estimation in Multiple Sclerosis.

A method for incorporating prior knowledge into the fuzzy connectedness image segmentation framework is presented. This prior knowledge is in the form of probabilistic feature distribution and feature size maps, in a standard anatomical space, and "intensity hints" selected by the user that allow for a skewed distribution of the feature intensity characteristics. The fuzzy affinity between pixels is modified to encapsulate this domain knowledge. The method was tested by using it to segment brain lesions in patients with multiple sclerosis, and the results compared to an established method for lesion outlining based on edge detection and contour following. With the fuzzy connections (FC) method, the user is required to identify each lesion with a mouse click, to provide a set of seed pixels. The algorithm then grows the features from the seeds to define the lesions as a set of objects with fuzzy connectedness above a pre-set threshold. The FC method gave improved inter-observer reproducibility of lesion volumes, and the set of pixels determined to be lesion was more consistent compared to the contouring method. The operator interaction time required to evaluate one subject was reduced from an average of 111 minutes with contouring to 16 minutes with the FC method

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact

Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p p p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features In this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignment This could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics</p