Faculty Bibliography

Despite the severity of coronavirus disease 2019 (COVID-19) being more frequently related to acute respiratory distress syndrome and acute cardiac and renal injuries, thromboembolic events have been increasingly reported. We report a unique series of young patients with COVID-19 presenting with cerebral venous system thrombosis. Three patients younger than 41 years of age with confirmed Severe Acute Respiratory Syndrome coronavirus 2 (SARS-Cov-2) infection had neurologic findings related to cerebral venous thrombosis. They were admitted during the short period of 10 days between March and April 2020 and were managed in an academic institution in a large city. One patient had thrombosis in both the superficial and deep systems; another had involvement of the straight sinus, vein of Galen, and internal cerebral veins; and a third patient had thrombosis of the deep medullary veins. Two patients presented with hemorrhagic venous infarcts. The median time from COVID-19 symptoms to a thrombotic event was 7 days (range, 2-7 days). One patient was diagnosed with new-onset diabetic ketoacidosis, and another one used oral contraceptive pills. Two patients were managed with both hydroxychloroquine and azithromycin; one was treated with lopinavir-ritonavir. All patients had a fatal outcome. Severe and potentially fatal deep cerebral thrombosis may complicate the initial clinical presentation of COVID-19. We urge awareness of this atypical manifestation.

The T2-FLAIR mismatch sign is an easily detectable imaging sign on routine clinical MRI studies that suggests diagnosis of IDH-mutant 1p/19q non-codeleted gliomas. Multiple independent studies show that the T2-FLAIR mismatch sign has near-perfect specificity, but low sensitivity, for diagnosing IDH-mutant astrocytomas. Thus, the T2-FLAIR mismatch sign represents a non-invasive radiogenomic diagnostic finding with potential clinical impact. Recently, false positive cases have been reported, many related to variable application of the sign's imaging criteria, differences in image acquisition as well as to differences in the included patient populations. Here we summarize the imaging criteria for the T2-FLAIR mismatch sign, review similarities and differences between the multiple validation studies, outline strategies to optimize its clinical use, and discuss potential opportunities to refine imaging criteria in order to maximize its impact in glioma diagnostics.

BACKGROUND AND PURPOSE/OBJECTIVE:Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status. MATERIALS AND METHODS/METHODS:Radiomic features (n = 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (n = 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (n = 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and IDH status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics. RESULTS:The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501-0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003-0.209). CONCLUSION/CONCLUSIONS:Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas. KEY POINTS/CONCLUSIONS:• Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.

During the height of the recent outbreak of coronavirus 19 (COVID-19) in New York City, almost all the hospital emergency departments were inundated with patients with COVID-19, who presented with typical fever, cough, and dyspnea. A small number of patients also presented with either unrelated conditions (such as trauma) or other emergencies, and some of which are now known to be associated with COVID-19 (such as stroke). We report such a scenario in 17 patients who were admitted and investigated with CT spine imaging and CT angiography for nonpulmonary reasons (trauma = 13, stroke = 4). Their initial work-up did not suggest COVID-19 as a diagnosis but showed unsuspected/incidental lung findings, which led to further investigations and a diagnosis of COVID-19.

PURPOSE/OBJECTIVE:To identify significant prognostic magnetic resonance imaging (MRI) features and their prognostic value when added to clinical features in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. MATERIALS AND METHODS/METHODS:Preoperative MR images of 158 patients (discovery set = 112, external validation set = 46) with IDHwt lower-grade gliomas (WHO grade II or III) were retrospectively analyzed using the Visually Accessible Rembrandt Images feature set. Radiologic risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator and elastic net. Multivariable Cox regression analysis, including age, Karnofsky Performance score, extent of resection, WHO grade, and RRS, was performed. The added prognostic value of RRS was calculated by comparing the integrated area under the receiver operating characteristic curve (iAUC) between models with and without RRS. RESULTS:The presence of cysts, pial invasion, and cortical involvement were favorable prognostic factors, while ependymal extension, multifocal or multicentric distribution, nonlobar location, proportion of necrosis > 33%, satellites, and eloquent cortex involvement were significantly associated with worse prognosis. RRS independently predicted survival and significantly enhanced model performance for survival prediction when integrated to clinical features (iAUC increased to 0.773-0.777 from 0.737), which was successfully validated on the validation set (iAUC increased to 0.805-0.830 from 0.735). CONCLUSION/CONCLUSIONS:MRI features associated with prognosis in patients with IDHwt lower-grade gliomas were identified. RRSs derived from MRI features independently predicted survival and significantly improved performance of survival prediction models when integrated into clinical features. KEY POINTS/CONCLUSIONS:• Comprehensive analysis of MRI features conveys prognostic information in patients with isocitrate dehydrogenase wild-type lower-grade gliomas. • Presence of cysts, pial invasion, and cortical involvement of the tumor were favorable prognostic factors. • Radiological phenotypes derived from MRI independently predict survival and have the potential to improve survival prediction when added to clinical features.

OBJECTIVE:To investigate the incidence and spectrum of neuroimaging findings and their prognostic role in hospitalized COVID-19 patients in New York City. METHODS:This is a retrospective cohort study of 3218 COVID-19 confirmed patients admitted to a major healthcare system (three hospitals) in New York City between March 1, 2020 and April 13, 2020. Clinical data were extracted from electronic medical records, and particularly data of all neurological symptoms were extracted from the imaging reports. Four neuroradiologists evaluated all neuroimaging studies for acute neuroimaging findings related to COVID-19. RESULTS:14.1% of admitted COVID-19 patients had neuroimaging and this accounted for only 5.5% of the total imaging studies. Acute stroke was the most common finding on neuro-imaging, seen in 92.5% of patients with positive neuro-imaging studies, and present in 1.1% of hospitalized COVID-19 patients. Patients with acute large ischemic and hemorrhagic stroke had much higher mortality risk adjusted for age, BMI and hypertension compared to those COVID-19 patients without neuroimaging. (Odds Ratio 6.02 by LR; Hazard Ratio 2.28 by CRR). CONCLUSIONS:Our study demonstrates acute stroke is the most common neuroimaging finding among hospitalized COVID-19 patients. Detection of an acute stroke is a strong prognostic marker of poor outcome. Our study also highlights the fact there is limited use of neuroimaging in these patients due to multiple logistical constraints.

BACKGROUND AND PURPOSE/OBJECTIVE:Despite the improved prognostic relevance of the 2016 WHO molecular-based classification of lower-grade gliomas, variability in clinical outcome persists within existing molecular subtypes. Our aim was to determine prognostically significant metrics on preoperative MR imaging for lower-grade gliomas within currently defined molecular categories. MATERIALS AND METHODS/METHODS:We undertook a retrospective analysis of 306 patients with lower-grade gliomas accrued from an institutional data base and The Cancer Genome Atlas. Two neuroradiologists in consensus analyzed preoperative MRIs of each lower-grade glioma to determine the following: tumor size, tumor location, number of involved lobes, corpus callosum involvement, hydrocephalus, midline shift, eloquent cortex involvement, ependymal extension, margins, contrast enhancement, and necrosis. Adjusted hazard ratios determined the association between MR imaging metrics and overall survival per molecular subtype, after adjustment for patient age, patient sex, World Health Organization grade, and surgical resection status. RESULTS:= .042) were associated with overall survival. CONCLUSIONS:wild-type lower-grade gliomas.

Recent advances in the understanding of the genetic makeup of gliomas have led to a paradigm shift in the diagnosis and classification of these tumors. Driven by these changes, the World Health Organization (WHO) introduced an update to its classification system of central nervous system (CNS) tumors in 2016. The updated glioma classification system incorporates molecular markers into tumor subgrouping, which has been shown to better correlate with tumor biology and behavior as well as patient prognosis than the previous purely histology-based classification system. Familiarity with this new classification scheme, the individual molecular markers, and corresponding imaging findings is critical for the radiologists who play an important role in diagnostic and surveillance imaging of patients with CNS tumors. The goals of this article are to review these updates to the WHO classification of CNS tumors with a focus on adult gliomas, provide an overview of key genomic markers of gliomas, and review imaging features pertaining to various genomic subgroups of adult gliomas.