Hypothalamic Lipoma: Outcome of an Intracranial Developmental Lesion

Background. Hypothalamic lipomas are benign developmental lesions that tend to be discovered incidentally. This article describes the radiological features, outcome, and the postulated theories behind hypothalamic lipomas development. Methods. The electronic archive of neurosurgery was retrospectively reviewed. All patients with a neuroradiological diagnosis of hypothalamic lipoma, between 2005 and 2020, were included. Results. Out of 246 patients with intracranial lipomas, a total of six patients with hypothalamic lipomas have been identified. On computed tomography images, one of the hypothalamic lipomas demonstrated calcification. On magnetic resonance imaging, peripheral enhancement after contrast administration was noted in one of the lesions. Considering the benign nature of the lesions, neurosurgical intervention was not indicated. Conclusion. The majority of patients with hypothalamic lipomas are asymptomatic and undergo brain imaging for other indications. Although uncommon, such developmental lesions can be identified in the general population, especially with the advancement of neuroimaging techniques.

Refining the mutational spectrum and gene–phenotype correlates in pontocerebellar hypoplasia: results of a multicentric study

BackgroundPontocerebellar hypoplasias (PCH) comprise a group of genetically heterogeneous disorders characterised by concurrent hypoplasia of the pons and the cerebellum and variable clinical and imaging features. The current classification includes 13 subtypes, with ~20 known causative genes. Attempts have been made to delineate the phenotypic spectrum associated to specific PCH genes, yet clinical and neuroradiological features are not consistent across studies, making it difficult to define gene-specific outcomes.MethodsWe performed deep clinical and imaging phenotyping in 56 probands with a neuroradiological diagnosis of PCH, who underwent NGS-based panel sequencing of PCH genes and MLPA for CASK rearrangements. Next, we conducted a phenotype-based unsupervised hierarchical cluster analysis to investigate associations between genes and specific phenotypic clusters.ResultsA genetic diagnosis was obtained in 43 probands (77%). The most common causative gene was CASK, which accounted for nearly half cases (45%) and was mutated in females and occasionally in males. The European founder mutation p.Ala307Ser in TSEN54 and pathogenic variants in EXOSC3 accounted for 18% and 9% of cases, respectively. VLDLR, TOE1 and RARS2 were mutated in single patients. We were able to confirm only few previously reported associations, including jitteriness and clonus with TSEN54 and lower motor neuron signs with EXOSC3. When considering multiple features simultaneously, a clear association with a phenotypic cluster only emerged for EXOSC3.ConclusionCASK represents the major PCH causative gene in Italy. Phenotypic variability associated with the most common genetic causes of PCH is wider than previously thought, with marked overlap between CASK and TSEN54-associated disorders.

The etiology, manifestations, and therapy of chronic cerebrovascular diseases

The paper presents the current etiopathogenetic classification of chronic cerebrovascular diseases (CVD) and discusses the role of hypertension, cerebral amyloid angiopathy, and genetically determined syndromes in the development of this pathological condition. It gives recommendations for the neuroradiological diagnosis of chronic CVD in accordance with the international standards. The paper discusses the clinical manifestations of chronic CVD, primarily vascular cognitive impairment. It discusses international guidelines for the examination and treatment of patients with chronic CVD, as well as the rules for stroke prevention in this patient cohort. The possibilities of pathogenetically based therapy in decreasing the severity of vascular cognitive impairment in the presence of chronic CVD are also highlighted.

Analysis of MRI Data in Diagnostic Neuroradiology

Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.