Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.

Gliomas are the most frequent primary tumors of the brain. They can be divided into grade II-IV astrocytomas and grade II-III oligodendrogliomas, based on their histomolecular profile. The prognosis and treatment is highly dependent on grade and well-identified prognostic and/or predictive molecular markers. Multi-parametric MRI, including diffusion weighted imaging, perfusion, and MR spectroscopy, showed increasing value in the non-invasive characterization of specific molecular subsets of gliomas. Radiolabeled amino-acid analogues, such as 18F-FET, have also been proven valuable in glioma imaging. These tracers not only contribute in the diagnostic process by detecting areas of dedifferentiation in diffuse gliomas, but this technique is also valuable in the follow-up of gliomas, as it can differentiate pseudo-progression from real tumor progression. Since multi-parametric MRI and 18F-FET PET are complementary imaging techniques, there may be a synergistic role for PET-MRI imaging in the neuro-oncological imaging of primary brain tumors. This could be of value for both primary staging, as well as during treatment and follow-up.

Background/Objectives: Asymptomatic meningiomas require frequent follow-up using MR imaging, with the standard of care being contrast-enhanced T1-weighted imaging (CE-T1WI) with Gadolinium-Based Contrast Agents (GBCAs). Limiting GBCA exposure reduces the environmental impact and limits possible gadolinium deposition in the brain. Therefore, the research objective was investigating the diagnostic accuracy of T2WI for evaluating significant meningioma growth (≥10% per year), using the CE-T1WI as reference standard. Methods: A total of 99 asymptomatic patients with the radiological diagnosis of meningioma and a minimum follow-up period of 11 months were retrospectively identified. Patients were scanned with various scanners in multiple hospitals. The maximum tumor diameter was measured in the transverse plane. Tumor growth was calculated in changes in millimeters and converted to percentages in the longest tumor diameter in the transverse plane. A paired-sample t-test was used to compare tumor growth on T2WI to CE-T1WI. The diagnostic accuracy of T2WI was determined by calculation of sensitivity, specificity, and positive and negative predictive values. Results: Mean follow-up time was 1.9 years. Significant tumor growth was found in 16 patients using T2WI, compared to 10 patients using CE-T1WI, which was not statistically significant. T2WI had a sensitivity of 80%, specificity of 90%, a positive predictive value of 47%, and negative predictive value of 98% for prediction of significant meningioma growth. Conclusions: T2WI was not inferior to CE-T1WI in the detection of significant tumor growth in asymptomatic meningioma and therefore can be used in follow-up to reduce gadolinium exposure.

Background: Ganglioglioma (GG) and dysembryoplastic neuroepithelial tumor (DNET) belong to the group of low-grade epilepsy-associated tumors (LEAT) and are the most prevalent tumor types found in patients undergoing epilepsy surgery. Histopathological differentiation between GG and DNET can be difficult on biopsies due to limited tumor tissue. Case Description: Here, we present a rare case where a low-grade tumor was initially classified as DNET, based on biopsy findings and unfortunately dedifferentiated within 10 years into a glioblastoma multiforme. After gross total resection, the initial tumor was reclassified as GG. Conclusion: This case illustrates the diagnostic challenges of LEAT, especially on biopsy material. Therefore, we advocate to counsel for complete resection and histopathological diagnosis utilizing tumor markers to confirm the nature of the tumor and to advice type of follow-up and eventual concurrent treatment.