Imaging of Fulminant Demyelinating Disorders of the Central Nervous System

Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme

Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

A Review of Clinical and Imaging Findings in Tumefactive Demyelination

OBJECTIVE. Tumefactive demyelination mimics primary brain neoplasms on imaging, often necessitating brain biopsy. This article reviews the literature for the clinical and radiologic findings of tumefactive demyelination in various disease processes to facilitate identification of tumefactive demyelination on imaging. CONCLUSION. Both clinical and radiologic findings must be integrated to distinguish tumefactive demyelinating lesions from similarly appearing lesions on imaging. Further research on the immunopathogenesis of tumefactive demyelination and associated conditions will elucidate their interrelationship.

Intracranial Abnormalities with Diffusion Restriction

Multiple pathologic conditions can cause changes in the random movement of water, which can be detected with diffusion-weighted imaging (DWI). DWI plays a powerful clinical role in detecting restricted diffusion associated with acute brain infarction. Other disorders can also result in restricted diffusion. This article focuses on showing examples of common and uncommon disorders that have restricted diffusion secondary to cytotoxic and/or intramyelinic edema. These disorders include ischemia, infection, noninfectious demyelinating diseases, genetic mutations affecting metabolism, acquired metabolic disorders, toxic or drug exposures, neoplasms and tumorlike lesions, radiation treatment, trauma, and denervation.

Heterogeneity of Baló's concentric sclerosis: a study of eight cases with different therapeutic concepts

Background

Baló’s Concentric Sclerosis (BCS) is a rare heterogeneous demyelinating disease with a variety of phenotypes on Magnetic Resonance Imaging (MRI). Existing literature lacks data especially on the therapeutic approach of the disease which we intended to elucidate by means of suggesting a new possible BCS classification and introducing different therapeutic concepts based on each BCS-subgroup characteristics.

Methods

We present a retrospective study of eight treated patients with BCS-type lesions, emphasizing on MRI characteristics and differences on therapeutic maneuvers.

Results

Data analysis showed: at disease onset the BCS-type lesion was tumefactive (size ≥2 cm) in 6 patients, with a mean size of 2.7 cm (± 0.80 SD); a coexistence of MS-like plaques on brain MRI was identified in 7 patients of our cohort. The mean age was 26.3 years (±7.3 SD) at disease onset and the mean follow-up period was 56.8 months (range 9–132 months). According to radiological characteristics and response to therapies, we further categorized them into 3 subgroups: a) Group-1; BCS with or without coexisting nonspecific white matter lesions; poor response to intravenous methylprednisolone (IVMP); treated with high doses of immunosuppressive agents (4 patients), b) Group-2; BCS with typical MS lesions; good response to IVMP; treated with MS-disease modifying therapies (2 patients), c) Group-3; BCS with typical MS lesions; poor response to IVMP; treated with rituximab (2 patients).

Conclusions

Our study introduces a new insight regarding the categorization of BCS into three subgroups depending on radiological features at onset and during the course of the disease, in combination with the response to different immunotherapies. Immunosuppressive agents such as cyclophosphamide are usually effective in BCS. However, therapeutic alternatives like anti-CD20 monoclonal antibodies or more classical disease-modifying MS therapies can be considered when BCS has also mixed lesions similar to MS. Future studies with a larger sample size are necessary to further establish these findings, thus leading to better treatment algorithms and improved clinical outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-020-01971-2.