Emerging glioneuronal and neuronal tumors: case-based review

Multinodular and vacuolating neuronal tumor in the thalamus: case report and systematic review of literature

The authors present the first reported case of MVNT in the thalamus in a 60-year-old man with a 20-year history of epilepsy and recent progressive neurological decline presented for neurosurgical evaluation for a non-enhancing mass predominantly in the right thalamus presumed to be a low-grade glioma. The tumor was subtotally resected using a left contralateral interhemispheric transcallosal approach. Histological and molecular assessment revealed an MVNT with MAPK pathway-activating mutation. The authors also conducted a systematic review of pathology-proven cases of MVNT to provide an up-to-date overview of the literature on the localization, presenting symptoms, and recurrence of this tumor.

Bright diffusion sign: A sensitive and specific radiologic biomarker for multinodular and vacuolating neuronal tumor

BACKGROUND AND PURPOSE

Accurate differentiation between multinodular and vacuolating neuronal tumor (MVNT) and dysembryoplastic neuroepithelial tumor (DNET) is important for treatment decision-making. We aimed to develop an accurate radiologic diagnostic model for differentiating MVNT from DNET using T2WI and diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

A total of 56 patients (mean age, 47.48±17.78 years; 31 women) diagnosed with MVNT (n = 37) or DNET (n = 19) who underwent brain MRI, including T2WI and DWI, were included. Two board-certified neuroradiologists performed qualitative (bubble appearance, cortical involvement, bright diffusion sign, and bright apparent diffusion coefficient [ADC] sign) and quantitative (nDWI and nADC) assessments. A diagnostic tree model was developed with significant and reliable imaging findings using an exhaustive chi-squared Automatic Interaction Detector (CHAID) algorithm.

RESULTS

In visual assessment, the imaging features that showed high diagnostic accuracy and interobserver reliability were the bright diffusion sign and absence of cortical involvement (bright diffusion sign: accuracy, 94.64 %; sensitivity, 91.89 %; specificity, 100.00 %; interobserver agreement, 1.00; absence of cortical involvement: accuracy, 92.86 %; sensitivity, 89.19 %; specificity, 100.00 %; interobserver agreement, 1.00). In quantitative analysis, nDWI was significantly higher in MVNT than in DENT (1.52 ± 0.34 vs. 0.91 ± 0.27, p < 0.001), but the interobserver agreement was fair (intraclass correlation coefficient = 0.321). The overall diagnostic accuracy of the tree model with visual assessment parameters was 98.21 % (55/56).

CONCLUSION

The bright diffusion sign and absence of cortical involvement are accurate and reliable imaging findings for differentiating MVNT from DNET. By using simple, intuitive, and reliable imaging findings, such as the bright diffusion sign, MVNT can be accurately differentiated from DNET.

Case report: Rare case of multinodular and vacuolar neuronal tumors in the cerebellum

Multinodular and vacuolar neuronal tumor (MVNT) is a rare and benign neuroepithelial tumor. Most reports describe tumors located in the cerebral hemisphere. A literature review found that 15 cases were located in the posterior cranial fossa, but all lacked pathological evidence. In this case, a patient sought medical attention due to insomnia and irritability. Neuroepithelial tumors were found in the imaging, and the patient underwent radiation therapy. Three years later, malignant tumors were found upon imaging examination. After surgical resection and pathological testing, MVNT occurring in the cerebellum was diagnosed. MVNT is rare in the cerebellum, and direct imaging diagnosis becomes difficult after treatment. Therefore, our report of this case helps to further accurate understanding of the imaging, pathological, and molecular genetic changes occurring before and after MVNT treatment, and will improve the accuracy of pre-treatment diagnosis and reduce the likelihood of overtreatment.

Brain Tumor Classification by Methylation Profile

The goal of the methylation classifier in brain tumor classification is to accurately classify tumors based on their methylation profiles. Accurate brain tumor diagnosis is the first step for healthcare professionals to predict tumor prognosis and establish personalized treatment plans for patients. The methylation classifier can be used to perform classification on tumor samples with diagnostic difficulties due to ambiguous histology or mismatch between histopathology and molecular signatures, i.e., not otherwise specified (NOS) cases or not elsewhere classified (NEC) cases, aiding in pathological decision-making. Here, the authors elucidate upon the application of a methylation classifier as a tool to mitigate the inherent complexities associated with the pathological evaluation of brain tumors, even when pathologists are experts in histopathological diagnosis and have access to enough molecular genetic information. Also, it should be emphasized that methylome cannot classify all types of brain tumors, and it often produces erroneous matches even with high matching scores, so, excessive trust is prohibited. The primary issue is the considerable difficulty in obtaining reference data regarding the methylation profile of each type of brain tumor. This challenge is further amplified when dealing with recently identified novel types or subtypes of brain tumors, as such data are not readily accessible through open databases or authors of publications. An additional obstacle arises from the fact that methylation classifiers are primarily research-based, leading to the unavailability of charging patients. It is important to note that the application of methylation classifiers may require specialized laboratory techniques and expertise in DNA methylation analysis.