Tumefactive demyelinating lesions versus CNS neoplasms, a comparative study.

BACKGROUND: Differentiating tumefactive demyelinating lesions (TDL) from neoplasms of the central nervous system continues to be a diagnostic dilemma in many cases. OBJECTIVE: Our study aimed to examine and contrast the clinical and radiological characteristics of TDL, high-grade gliomas (HGG) and primary CNS lymphoma (CNSL). METHOD: This was a retrospective review of 66 patients (23 TDL, 31 HGG and 12 CNSL). Clinical and laboratory data were obtained. MRI brain at presentation were analyzed by two independent, blinded neuroradiologists. RESULTS: Patients with TDLs were younger and predominantly female. Sensorimotor deficits and ataxia were more common amongst TDL whereas headaches and altered mental status were associated with HGG and CNSL. Compared to HGG and CNSL, MRI characteristics supporting TDL included relatively smaller size, lack of or mild mass effect, incomplete peripheral rim enhancement, absence of central enhancement or restricted diffusion, lack of cortical involvement, and presence of remote white matter lesions on the index scan. Paradoxically, some TDLs may present atypically or radiologically mimic CNS lymphomas. CONCLUSION: Careful evaluation of clinical and radiological features helps in differentiating TDLs at first presentation from CNS neoplasms.

The Spectrum of Vessel Wall Imaging (VWI) Findings in COVID-19-Associated Neurological Syndromes: A Review.

Since the start of the pandemic, there have been extensive studies from all over the world reporting on coronavirus disease 2019 (COVID-19)-associated neurological syndromes. Although initially thought of as primarily a respiratory pathogen, it became increasingly clear that the virus does have other systemic manifestations, including on the neurological system. Since then, the discovery of the many neuroimaging features of COVID-19-associated neurological syndromes have puzzled researchers and physicians in terms of interpretation, and how best to manage these findings to benefit patients. We sought to review the neuroimaging findings of COVID-19-associated neurological syndromes, particularly the vessel wall imaging (VWI) features, in the hope of finding a common feature that would better guide physicians in terms of further management of this group of patients. We will also look into the potential pitfalls of interpreting the VWI findings in these patients.

Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy.

White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value < 0.001), respectively, and outperforming three leading algorithms (Trimmed Mean Outlier Detection, Lesion Prediction Algorithm, and SALEM-LS) in five of the six established key metrics defined in the MICCAI Grand Challenge. By facilitating more accurate segmentation of subtle WML, this approach may enable earlier diagnosis and intervention.