Sjogren-Larsson syndrome brain volumetric reductions demonstrated with an automated software

BACKGROUND

Sjogren-Larsson syndrome (SLS) is a neurocutaneous disease with an autosomal recessive inheritance, caused by mutations in the gene that encodes fatty aldehyde dehydrogenase (ALDH3A2), clinically characterized by ichthyosis, spastic diplegia, and cognitive impairment. Brain imaging plays an essential role in the diagnosis, demonstrating a nonspecific leukoencephalopathy. Data regarding brain atrophy and grey matter involvement is scarce and discordant.

OBJECTIVE

We performed a volumetric analysis of the brain of two siblings with SLS with the aim of detecting deep grey matter nuclei, cerebellar grey matter, and brainstem volume reduction in these patients.

METHODS

Volume data obtained from the brain magnetic resonance imaging (MRI) of the two patients using an automated segmentation software (Freesurfer) was compared with the volumes of a healthy control group.

RESULTS

Statistically significant volume reduction was found in the cerebellum cortex, the brainstem, the thalamus, and the pallidum nuclei.

CONCLUSION

Volume reduction in grey matter leads to the hypothesis that SLS is not a pure leukoencephalopathy. Grey matter structures affected in the present study suggest a dysfunction more prominent in the thalamic motor pathways.

Neurological manifestations in thrombotic microangiopathy: Imaging features, risk factors and clinical course

Background and purpose

Thrombotic microangiopathy (TMA) is a group of microvascular occlusive disorders that presents with neurological involvement in up to 87% of the cases. Although the central nervous system (CNS) is an important target organ in TMA, the role of neurological manifestations in the disease clinical course is not well established. In this study, we described the neurological manifestations and CNS radiological aspects in patients with a first, acute TMA event. We also examined the association between severe neurological involvement and adverse clinical outcomes in TMA.

Methods

A cohort of patients diagnosed with a first TMA event between 1995 and 2016 was included, their medical charts and imaging tests were retrospectively evaluated.

Results

A total of 49 patients were included, 85.7% were women and the mean age was 36.5 years-old (SD 13.0). Neurological manifestations were described in 85.7% of the patients, most of them (88%) were considered severe and consisted of confusion, compromised sensorimotor function, stupor, seizures, and personality change. Imaging tests were performed in 62% of the patients with neurological manifestations and detected acute CNS lesions, such as posterior reversible encephalopathy syndrome, hemorrhagic and ischemic stroke were observed, in 7 (27%) of them. While the need for intensive care unit admission was greater and longer among patients with severe neurological manifestations, the number of plasma exchange sessions, the total duration of hospitalization and in-hospital death were similar between groups.

Conclusions

Severe neurological manifestations are common in first TMA events and are responsible for a worse disease presentation at admission. While the effect of neurological manifestations on acute TMA clinical course seems to be modest, these manifestations may have an important impact on the development of chronic cognitive impairment, which highlights the need for proper diagnosis and treatment.

Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?

Background:

Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions.

Methods:

In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI.

Results:

The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912).

Conclusion:

The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.