Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review.

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.

3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.

Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient's mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient's mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. KEY POINTS: • 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients. • The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.

Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media.

Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.

Gantry-needle-target alignment technique for CT-guided needle approaches to the skull base and cranio-cervical junction.

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.

Understanding Language Reorganization With Neuroimaging: How Language Adapts to Different Focal Lesions and Insights Into Clinical Applications.

When the language-dominant hemisphere is damaged by a focal lesion, the brain may reorganize the language network through functional and structural changes known as adaptive plasticity. Adaptive plasticity is documented for triggers including ischemic, tumoral, and epileptic focal lesions, with effects in clinical practice. Many questions remain regarding language plasticity. Different lesions may induce different patterns of reorganization depending on pathologic features, location in the brain, and timing of onset. Neuroimaging provides insights into language plasticity due to its non-invasiveness, ability to image the whole brain, and large-scale implementation. This review provides an overview of language plasticity on MRI with insights for patient care. First, we describe the structural and functional language network as depicted by neuroimaging. Second, we explore language reorganization triggered by stroke, brain tumors, and epileptic lesions and analyze applications in clinical diagnosis and treatment planning. By comparing different focal lesions, we investigate determinants of language plasticity including lesion location and timing of onset, longitudinal evolution of reorganization, and the relationship between structural and functional changes.

Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis.

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.

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.

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.

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.

Magnetic resonance imaging of pilocytic astrocytomas in adults with histopathologic correlation: a report of six consecutive cases.

Pilocytic astrocytoma is a WHO grade I tumor usually diagnosed in pediatric patients, and rarely encountered in the adult population. Therefore, available information about the magnetic resonance imaging characteristics of adult pilocytic astrocytoma is scarce. We report on the MRI features and corresponding histopathologic findings of six consecutive aPA cases diagnosed. The tumors were encountered in both infra- and supratentorial compartments, and their MRI characteristics were quite heterogeneous. Features included the typical solid-cystic appearance located in the cerebellum as well as the relatively unusual multifocal and/or hemorrhagic features located intra-ventricularly. The aPA MRI characteristics are remarkably variable, and might mimic those of higher grade tumors in adult patients.

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.

Arterial Spin Labeling MRI in Carotid Stenosis: Arterial Transit Artifacts May Predict Symptoms.

BackgroundStenosis of the internal carotid artery has a higher risk for stroke. Many investigations have focused on structure and plaque composition as signs of plaque vulnerability, but few studies have analyzed hemodynamic changes in the brain as a risk factor.PurposeTo use 3-T MRI methods including contrast material-enhanced MR angiography, carotid plaque imaging, and arterial spin labeling (ASL) to identify imaging parameters that best help distinguish between asymptomatic and symptomatic participants with carotid stenosis.Materials and MethodsParticipants with carotid stenosis from two ongoing prospective studies who underwent ASL and carotid plaque imaging with use of 3-T MRI in the same setting from 2014 to 2018 were studied. Participants were assessed clinically for recent symptoms (transient ischemic attack or stroke) and divided equally into symptomatic and nonsymptomatic groups. Reviewers were blinded to the symptomatic status and MRI scans were analyzed for the degree of stenosis, plaque surface structure, presence of intraplaque hemorrhage (IPH), circle of Willis collaterals, and the presence and severity of arterial transit artifacts (ATAs) at ASL imaging. MRI findings were correlated with symptomatic status by using t tests and the Fisher exact test.ResultsA total of 44 participants (mean age, 71 years ± 10 [standard deviation]; 31 men) were evaluated. ATAs were seen only in participants with greater than 70% stenosis (16 of 28 patients; P < .001) and were associated with absence of anterior communicating artery (13 of 16 patients; P = .003). There was no association between history of symptoms and degree of stenosis (27 patients with ≥70% stenosis and 17 patients with <70%; P = .54), IPH (12 patients with IPH and 32 patients without IPH; P = .31), and plaque surface structure (17 patients with irregular or ulcerated plaque and 27 with smooth plaque; P = .54). Participants with ATAs (n = 16) were more likely to be symptomatic than were those without ATAs (n = 28) (P = .004). Symptomatic status also was associated with the severity of ATAs (P = .002).ConclusionArterial transit artifacts were the only factor associated with recent ischemic symptoms in participants with carotid stenosis. The degree of stenosis, plaque ulceration, and intraplaque hemorrhage were not associated with symptomatic status.© RSNA, 2020Online supplemental material is available for this article.See also the editorial by Zaharchuk in this issue.

Non-congenital viral infections of the central nervous system: from the immunocompetent to the immunocompromised child.

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.

Central Nervous System involvement in tuberculosis: An MRI study considering differences between patients with and without Human Immunodeficiency Virus 1 infection.

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.

Prediction of survival in patients affected by glioblastoma: histogram analysis of perfusion MRI.

PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: The histogram analysis of perfusion MRI proved to be a valid method to predict survival in patients affected by glioblastoma.

Pituitary apoplexy: an update on clinical and imaging features.

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.