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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 −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.

Arterial transit artifacts at arterial spin-labeling MRI were the only factor associated with recent ischemic symptoms in participants with carotid stenosis.

Background Magnetic resonance imaging (MRI) is largely used in the diagnosis of central nervous system involvement of tuberculosis (CNSTB), yet there is no MRI comparison study between HIV+ and HIV− patients with CNSTB. The aim of the present study was to identify MRI differences in CNSTB between HIV+ and HIV− patients and possibly find early characteristics that could raise the suspect of this disease. Methods We included all patients admitted in our institution between 2011 and 2018 with confirmed diagnosis of CNSTB, and MRI performed in the first week. Patients with preexisting brain pathology or immunodeficiency not HIV related were excluded. We compared CNSTB MRI features between the two groups. Results Sixty-nine patients were included (19 HIV+; 50 HIV−). Findings in HIV+ group: 6 lung TB, 5 hydrocephalus, 4 meningeal enhancement, 6 stroke, 2 hemorrhages, and 10 tuberculomas. HIV− group: 22 lung tuberculosis, 15 hydrocephalus, 21 meningeal enhancement, 5 stroke, 4 hemorrhages, 20 tuberculomas. The only statistically significant difference between the two groups was in the stroke occurrence, more frequent in the HIV+ group (P = .028), all involving the basal ganglia. Conclusions Stroke involving the basal ganglia best differentiates CNSTB patients who are HIV+ from those HIV−. This finding was not correlated with meningeal enhancement suggesting that small arteries involvement might precede it. Therefore, we think that HIV+ patients with a new onset of stroke should be evaluated for CNSTB. Follow-up MRI should also be planned since meningeal enhancement might appear in later stages of the disease.

PURPOSE CT-guided percutaneous procedures involving the skull base and atlanto-axial cervical spine pose particular challenges due to high density of vital vascular and nervous structures and because the ideal needle trajectory often has a cranio-caudal obliquity different from the axial scan plane. We describe how the variable CT gantry tilt, combined with gantry-needle-target alignment technique, is used to obtain precise and safe needle placement in conventional and non-conventional approaches to the skull base and the atlanto-axial spine. METHODS We retrospectively analyzed consecutive CT-guided needle accesses to the skull base and atlanto-axial spine performed for tissue sampling through fine-needle aspirates and core biopsies, cementoplasty of neoplastic lytic lesions of atlanto-axial spine, pain management injections, and dural puncture for cerebro-spinal fluid sampling. All the accesses were performed with the gantry-needle-target alignment technique. Procedural complications were recorded. RESULTS Thirty-nine CT-guided procedures were analyzed. Paramaxillary approach was used in 15 cases, postero-lateral in 11, subzygomatic in 3. Nine non-conventional approach were performed: submastoid in 3 cases, suprazygomatic in 2, trans-nasal in 2, trans-mastoid in 1, and trans-auricular in 1. Two peri-procedural complications occurred: one asymptomatic and one resolved within 24 h. All the procedures were successfully completed with successful needle access to the target. CONCLUSION The gantry tilt and gantry-needle-target alignment technique allows to obtain double-oblique needle accesses for CT-guided procedures involving the skull base and atlanto-axial cervical spine, minimizing uncertainty of needle trajectory and obtaining safe needle placement in conventional and non-conventional approaches.

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.

BACKGROUND Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). Many machine learning (ML) radiomic models have been developed, mostly employing single classifiers with variable results. However, comparative analyses of different ML models for clinically-relevant tasks are lacking in the literature. OBJECTIVE We aimed to compare well-established ML learning classifiers, including single and ensemble learners, to predict clinically-relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) amplification and Ki-67 expression in HGG patients, based on radiomic features from conventional and advanced MRI. Our objective was to identify the best algorithm for each task in terms of accuracy of the prediction performance. METHODS 156 adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics, and selected through Boruta algorithm. A Grid Search algorithm was applied when computing 4 times K-fold cross validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as Area Under The Curve-Receiver Operating Characteristics (AUC-ROC). RESULTS Ensemble classifiers showed the best performance across tasks. xGB obtained highest accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFRvIII amplification (81,6%). CONCLUSIONS Best performing features shed light on possible correlations between MRI and tumor histology.

PURPOSEThe authors purpose was to evaluate ADC and rCBV values in the enhanced lesion, in the peritumoral area and in distal oedema using a Volume of Interest (VOI) based method and to analysed hemodynamic curves obtained from DSC perfusion MRI, in order to create a valid multiparametric MRI model for the differential diagnosis between Glioblastoma and solitary Brain Metastasis.MATERIALS AND METHODSForty-one patients (twenty glioblastomas and twenty-one single brain metastases) were retrospectively evaluated. MRI images were acquired before surgery, radiotherapy and chemotherapy. MRIs were analysed with Olea Sphere® 3.0 (Olea Medical, La Ciotat, France), in particular with diffusion, perfusion and volume of interest segmentation plug-ins. FLAIR, 3D T1 MP-RAGE images after gadolinium, ADC and rCBV maps for each patient were co-registered by the OleaSphere software; this was followed by visual inspection to ensure adequate alignment. Volumes of interest (VOIs) of the lesions were drawn on enhanced 3D T1 MP-RAGE avoiding cyst or necrotic degeneration, and then projected on ADC and rCBV co-registered maps. Another 2 VOIs were drawn in the region of hyperintense cerebral oedema, surrounding the lesion (GB or BM) visible on FLAIR images. The first VOI was drawn into perilesional oedema within 5mm around the enhancing tumor. The second VOI was drawn into residual oedema. Both VOIs were projected on ADC and rCBV maps. Perfusion curves were obtained for each lesion 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 to determine the optimal parameter in distinguishing GB from BM. RESULTSComparing all parameters evaluated for patients with GB and BM, the cerebral lesions were distinguishable with the mean ADC VOI- values of solid component, the PSR values and the mean and max rCBV values in the perilesional edema within 5mm around the enhancing tumor. 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-3 mm2 s-1 GB could be differentiated from solitary BM with sensitivity and specificity of 95% and 86%. CONCLUSIONWe can conclude that lower values of ADC in the enhancing tumor volume and a higher percentage of signal recovery in perfusion curves, associated with higher values of rCBV in the peritumoral edema closed to the lesion, are strongly indicative of GB than solitary BM.

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.

BACKGROUND AND PURPOSE Glioblastoma (GBM) is an aggressive primary CNS neoplasm with poor overall survival (OS) despite standard of care. On MRI, GBM is usually characterized by an enhancing portion (CET) (surgery target) and a nonenhancing surrounding (NET). Extent of resection is a long debated issue in GBM, with recent evidence suggesting that both CET and NET should be resected in

Non-congenital viral infections of the central nervous system in children can represent a severe clinical condition that needs a prompt diagnosis and management. However, the aetiological diagnosis can be challenging because symptoms are often nonspecific and cerebrospinal fluid analysis is not always diagnostic. In this context, neuroimaging represents a helpful tool, even though radiologic patterns sometimes overlap. The purpose of this pictorial essay is to suggest a schematic representation of different radiologic patterns of non-congenital viral encephalomyelitis based on the predominant viral tropism and vulnerability of specific regions: cortical grey matter, deep grey matter, white matter, brainstem, cerebellum and spine.

The identification of prognostic biomarkers plays a pivotal role in the management of glioblastoma. The aim of this study was to assess the role of magnetic resonance dynamic susceptibility contrast imaging (DSC-MRI) with histogram analysis in the prognostic evaluation of patients suffering from glioblastoma. Sixty-eight patients with newly diagnosed pathologically verified GBM were retrospectively evaluated. All patients underwent MRI investigations, including DSC-MRI, surgical procedure and received postoperative focal radiotherapy plus daily temozolomide (TMZ), followed by adjuvant TMZ therapy. Relative cerebral blood volume (rCBV) histograms were generated from a volume of interest covering the solid portions of the tumor and statistically evaluated for kurtosis, skewness, mean, median and maximum value of rCBV. To verify if histogram parameters could predict survival at 1 and 2 years, receiver operating characteristic (ROC) curves were obtained. Kaplan–Meier method was used to calculate patient’s overall survival. rCBV kurtosis and rCBV skewness showed significant differences between subjects surviving > 1 and > 2 years, According to ROC analysis, the rCBV kurtosis showed the best statistic performance compared to the other parameters; respectively, values of 1 and 2.45 represented an optimised cut-off point to distinguish subjects surviving over 1 or 2 years. Kaplan–Meier curves showed a significant difference between subjects with rCBV kurtosis values higher or lower than 1 (respectively 1021 and 576 days; Log-rank test: p = 0.007), and between subjects with rCBV kurtosis values higher or lower than 2.45 (respectively 802 and 408 days; Log-rank test: p = 0.001). The histogram analysis of perfusion MRI proved to be a valid method to predict survival in patients affected by glioblastoma.

Pituitary apoplexy (PA) is a rare and potentially fatal clinical condition presenting acute headache, vomiting, visual impairment, ophthalmoplegia, altered mental state and possible panhypopituitarism. It mostly occurs in patients with haemorrhagic infarction of the pituitary gland due to a pre-existing macroadenoma. Although there are pathological and physiological conditions that may share similar imaging characteristics, both clinical and imaging features can guide the radiologist towards the correct diagnosis, especially using magnetic resonance imaging (MRI). In this review, we will describe the main clinical and epidemiological features of PA, illustrating CT and MRI findings and discussing the role of imaging in the differential diagnosis, prognosis and follow-up. Teaching points • Headache, ophtalmoplegia and visual impairment are frequent symptoms of pituitary apoplexy. • CT is often the first imaging tool in PA, showing areas of hyperdensity within the sellar region. • MRI could confirm haemorrhage within the pituitary gland and compression on the optic chiasm. • Frequent simulating conditions are aneurysms, Rathke cleft cysts, craniopharingioma and mucocele. • The role of imaging is still debated and needs more studies.