Incomplete partition type II in its various manifestations: isolated, in association with EVA, syndromic, and beyond; a multicentre international study.

PURPOSE: Incomplete partition type II (IP-II) is characterized by specific histological features and radiological appearance. It may occur in isolation or in association with an enlarged vestibular aqueduct (EVA). Among those with IP-II and EVA, a subset has a diagnosis of Pendred syndrome. This study aimed to explore the prevalence of isolated IP-II, IP-II with EVA, and cases with a genetic or syndromic basis in our cohort. METHODS: From a large, multicentre database of dysplastic cochleae (446 patients, 892 temporal bones), those with imaging features of IP-II were examined in detail, including whether there was a genetic or syndromic association. RESULTS: A total of 78 patients with IP-II were identified. Among these, 55 patients had bilateral IP-II and EVA (only 12 with typical Mondini triad), 8 with bilateral IP-II and normal VA, 2 with bilateral IP-II and unilateral EVA, and 13 with unilateral IP-II (9 with unilateral EVA). Among the group with bilateral IP-II and bilateral EVA in whom genetic analysis was available, 14 out of 29 (48%) had SLC26A4 mutations and a diagnosis of Pendred syndrome, 1 had a FOXI1 mutation, and a few other genetic abnormalities; none had KCNJ10 pathogenic variants. CONCLUSION: Bilateral IP-II-bilateral EVA may be seen in the context of Pendred syndrome (SLC26A4 or FOXI1 mutations) but, in the majority of our cohort, no genetic abnormalities were found, suggesting the possibility of unknown genetic associations. IP-II in isolation (without EVA) is favored to be genetic when bilateral, although the cause is often unknown.

Extra-axial cranial nerve enhancement: a pattern-based approach.

Cranial nerve enhancement is a common and challenging MRI finding that requires a meticulous and systematic evaluation to identify the correct diagnosis. Literature mainly describes the various pathologies with the associated clinic-radiological characteristics, while the radiologist often needs a reverse approach that starts from the radiological findings to reach the diagnosis. Therefore, our aim is to provide a new and practical pattern-based approach to cranial nerve enhancement, which starts from the radiological findings and follows pattern-driven pipelines to navigate through multiple differential diagnoses, guiding the radiologist to reach the proper diagnosis. Firstly, we reviewed the literature and identified four patterns to categorize the main pathologies presenting with cranial nerve enhancement: unilateral linear pattern, bilateral linear pattern, unilateral thickened pattern, and bilateral thickened pattern. For each pattern, we describe the underlying pathogenic origin, and the main radiological features are displayed through high-quality MRI images and illustrative panels. A suggested MRI protocol for studying cranial nerve enhancement is also provided. In conclusion, our approach for cranial nerve enhancement aims to be an easy tool immediately applicable to clinical practice for converting challenging findings into specific pathological patterns.

Dural Tail Sign and Middle Meningeal Artery Hypertrophy in Glioblastoma: A Rarity?

BACKGROUND: Dural tail sign and increased caliber of branches of the external carotid artery (ECA) are common findings in meningioma and they have been rarely reported in intra-axial lesions. Anyway, some cases of glioblastoma (GBM) are reported in the literature, mostly superficially localized, characterized by these 2 findings and therefore, misdiagnosed with meningioma. The aim of this study is to verify the prevalence of dural tail sign and hypertrophy of middle meningeal artery (MMA) in a large cohort of GBMs. METHODS: 180 GBM patients were retrospectively evaluated. Deep or superficial localization of GBM was established and the presence of dural tail sign and hypertrophy of the ipsilateral MMA were assessed. The rate of tumor necrosis and the incidence of dural metastases during the radiological follow-up were also evaluated. Inter-rater reliability was calculated using Cohen's K-test. RESULTS: Dural tail sign and enlarged MMA were evident in 30% and 19% of 96 superficial GBM, respectively. Deep GBM did not present those signs. Only one patient developed dural metastasis at follow-up and no differences in terms of tumor necrosis and hypoxic biomarkers expression were evident among GBMs with and without dural and vessel signs. CONCLUSIONS: Dural tail sign and hypertrophy of the MMA in superficial GBM are more common than expected. They probably represent reactive rather than a neoplastic infiltration. Knowing these radiological signs may be important in terms of neurosurgery planning and avoiding excessive bleeding. Anyway, this hypothesis should be confirmed by a prospective neurosurgery studio.

Glioblastoma radiomics to predict survival: Diffusion characteristics of surrounding nonenhancing tissue to select patients for extensive resection.

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 <65 years old patients, regardless of other risk factors (i.e., molecular biomarkers). Our aim was to test a radiomic model for patient survival stratification in <65 years old patients, by analyzing MRI features of NET, to aid tumor resection. METHODS: Sixty-eight <65 years old GBM patients, with extensive CET resection, were selected. Resection was evaluated by manually segmenting CET on volumetric T1-weighted MRI pre and postsurgery (within 72 h). All patients underwent the same treatment protocol including chemoradiation. NET radiomic features were extracted with a custom version of Pyradiomics. Feature selection was performed with principal component analysis (PCA) and its effect on survival tested with Cox regression model. Twelve months OS discrimination was tested by t-test followed by logistic regression. Statistical significance was set at p<0.05. The most relevant features were identified from the component matrix. RESULTS: Five PCA components (PC1-5) explained 90% of the variance. PC5 resulted significant in the Cox model (p = 0.002; exp(B) = 0.686), at t-test (p = 0.002) and logistic regression analysis (p = 0.006). Apparent diffusion coefficient (ADC)-based features were the most significant for patient survival stratification. CONCLUSIONS: ADC radiomic features on NET predict survival after standard therapy and could be used to improve patient selection for more extensive surgery.

In Vivo Brain GSH: MRS Methods and Clinical Applications.

Glutathione (GSH) is an important antioxidant implicated in several physiological functions, including the oxidation-reduction reaction balance and brain antioxidant defense against endogenous and exogenous toxic agents. Altered brain GSH levels may reflect inflammatory processes associated with several neurologic disorders. An accurate and reliable estimation of cerebral GSH concentrations could give a clear and thorough understanding of its metabolism within the brain, thus providing a valuable benchmark for clinical applications. In this context, we aimed to provide an overview of the different magnetic resonance spectroscopy (MRS) technologies introduced for in vivo human brain GSH quantification both in healthy control (HC) volunteers and in subjects affected by different neurological disorders (e.g., brain tumors, and psychiatric and degenerative disorders). Additionally, we aimed to provide an exhaustive list of normal GSH concentrations within different brain areas. The definition of standard reference values for different brain areas could lead to a better interpretation of the altered GSH levels recorded in subjects with neurological disorders, with insights into the possible role of GSH as a biomarker and therapeutic target.

Cerebral Venous Thrombosis: A Challenging Diagnosis; A New Nonenhanced Computed Tomography Standardized Semi-Quantitative Method.

Cerebral venous sinus thrombosis (CVST) on non-contrast CT (NCCT) is often challenging to detect. We retrospectively selected 41 children and 36 adults with confirmed CVST and two age-matched control groups with comparable initial symptoms. We evaluated NCCT placing four small circular ROIs in standardized regions of the cerebral dural venous system. The mean and maximum HU values were considered from each ROI, and the relative percentage variations were calculated (mean % variation and maximum % variation). We compared the highest measured value to the remaining three HU values through an ad-hoc formula based on the assumption that the thrombosed sinus has higher attenuation compared with the healthy sinuses. Percentage variations were employed to reflect how the attenuation of the thrombosed sinus deviates from the unaffected counterparts. The attenuation of the affected sinus was increased in patients with CVST, and consequently both the mean % and maximum % variations were increased. A mean % variation value of 12.97 and a maximum % variation value of 10.14 were found to be useful to distinguish patients with CVST from healthy subjects, with high sensitivity and specificity. Increased densitometric values were present in the site of venous thrombosis. A systematic, blind evaluation of the brain venous system can assist radiologists in identifying patients who need or do not need further imaging.

AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?

Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers 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 vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six 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 ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.

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.