Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Purpose: The authors’ purpose was to create a valid multiparametric MRI model for the differential diagnosis between glioblastoma and solitary brain metastasis. Materials and methods: Forty-one patients (twenty glioblastomas and twenty-one brain metastases) were retrospectively evaluated. MRIs were analyzed with Olea Sphere^® 3.0. Lesions’ volumes of interest (VOIs) were drawn on enhanced 3D T1 MP-RAGE and projected on ADC and rCBV co-registered maps. Another two VOIs were drawn in the region of hyperintense cerebral edema, surrounding the lesion, respectively, within 5 mm around the enhancing tumor and into residual edema. Perfusion curves were obtained, and the value of signal recovery (SR) was reported. A two-sample T test was obtained to compare all parameters of GB and BM groups. Receiver operating characteristics (ROC) analysis was performed. Results: According to ROC analysis, the area under the curve was 88%, 78% and 74%, respectively, for mean ADC VOI values of the solid component, the mean and max rCBV values in the perilesional edema and the PSR. The cumulative ROC curve of these parameters reached an area under the curve of 95%. Using perilesional max rCBV > 1.37, PSR > 75% and mean lesional ADC < 1 × 10⁻³ mm² s⁻¹ GB could be differentiated from solitary BM (sensitivity and specificity of 95% and 86%). Conclusion: Lower values of ADC in the enhancing tumor, a higher percentage of SR in perfusion curves and higher values of rCBV in the peritumoral edema closed to the lesion are strongly indicative of GB than solitary BM.

Congenital infections represent a challenging and varied clinical scenario in which the brain is frequently involved. Therefore, fetal and neonatal neuro-imaging plays a pivotal role in reaching an accurate diagnosis and in predicting the clinical outcome. Congenital brain infections are characterized by various clinical manifestations, ranging from nearly asymptomatic diseases to syndromic disorders, often associated with severe neurological symptoms. Brain damage results from the complex interaction among the infectious agent, its specific cellular tropism, and the stage of development of the central nervous system at the time of the maternal infection. Therefore, neuroradiological findings vary widely and are the result of complex events. An early detection is essential to establishing a proper diagnosis and prognosis, and to guarantee an optimal and prompt therapeutic perinatal management. Recently, emerging infective agents (i.e., Zika virus and SARS-CoV2) have been related to possible pre- and perinatal brain damage, thus expanding the spectrum of congenital brain infections. The purpose of this pictorial review is to provide an overview of the current knowledge on fetal and neonatal brain neuroimaging patterns in congenital brain infections used in clinical practice.

The 2021 WHO (World Health Organization) classification of brain tumors incorporated the rapid advances in the molecular, genetic, and pathogenesis understanding of brain tumor pathogenesis, behavior, and treatment response. It revolutionized brain tumor classification by placing great emphasis on molecular types and completely splitting adult-type and pediatric-type diffuse gliomas. Brainstem gliomas (BSGs) are the leading primary tumors of the brainstem, although they are quite uncommon in adults compared with the pediatric population, representing less than 2% of adult gliomas. Surgery is not always the treatment of choice since resection is rarely feasible and does not improve overall survival, and biopsies are not generally performed since the location is treacherous. Therefore, MRI (Magnetic Resonance Imaging) without and with gadolinium administration represents the optimal noninvasive radiological technique to suggest brainstem gliomas diagnosis, plan a multidisciplinary treatment and for follow-up evaluations. The MRI protocol encompasses morphological sequences as well as functional and advanced sequences, such as DWI/ADC (Diffusion-Weighted Imaging/Apparent Diffusion Coefficient), DTI (Diffusion Tensor Imaging), PWI (Perfusion-Weighted Imaging), and MRS (Magnetic Resonance Spectroscopy), which improve the accuracy of the diagnosis of BSGs by adding substantial information regarding the cellularity, the infiltrative behavior toward the v fiber tracts, the vascularity, and the molecular changes. Brainstem gliomas have been divided into four categories on the basis of their MRI radiological appearance, including diffuse intrinsic low-grade gliomas, enhancing malignant gliomas, localized tectal gliomas, and other forms. The aim of our review is to provide insight into the role of advanced MRI sequences in the diagnosis and follow-up of adult brainstem gliomas.

Stereotactic radiosurgery (SRS) has transformed the management of brain metastases by achieving local tumor control, reducing toxicity, and minimizing the need for whole-brain radiation therapy (WBRT). This review specifically investigates radiation-induced changes in patients treated for metastasis, highlighting the crucial role of magnetic resonance imaging (MRI) in the evaluation of treatment response, both at very early and late stages. The primary objective of the review is to evaluate the most effective imaging techniques for assessing radiation-induced changes and distinguishing them from tumor growth. The limitations of conventional imaging methods, which rely on size measurements, dimensional criteria, and contrast enhancement patterns, are critically evaluated. In addition, it has been investigated the potential of advanced imaging modalities to offer a more precise and comprehensive evaluation of treatment response. Finally, an overview of the relevant literature concerning the interpretation of brain changes in patients undergoing immunotherapies is provided.