Association of Coronavirus Disease (COVID-19) With Large Vessel Occlusion Strokes: A Case-Control Study.

BACKGROUND. An increase in frequency of acute ischemic strokes has been observed among patients presenting with acute neurologic symptoms during the coronavirus disease (COVID-19) pandemic. OBJECTIVE. The purpose of this study was to investigate the association between COVID-19 and stroke subtypes in patients presenting with acute neurologic symptoms. METHODS. This retrospective case-control study included patients for whom a code for stroke was activated from March 16 to April 30, 2020, at any of six New York City hospitals that are part of a single health system. Demographic data (age, sex, and race or ethnicity), COVID-19 status, stroke-related risk factors, and clinical and imaging findings pertaining to stroke were collected. Univariate and multivariate analyses were conducted to evaluate the association between COVID-19 and stroke subtypes. RESULTS. The study sample consisted of 329 patients for whom a code for stroke was activated (175 [53.2%] men, 154 [46.8%] women; mean age, 66.9 ± 14.9 [SD] years). Among the 329 patients, 35.3% (116) had acute ischemic stroke confirmed with imaging; 21.6% (71) had large vessel occlusion (LVO) stroke; and 14.6% (48) had small vessel occlusion (SVO) stroke. Among LVO strokes, the most common location was middle cerebral artery segments M1 and M2 (62.0% [44/71]). Multifocal LVOs were present in 9.9% (7/71) of LVO strokes. COVID-19 was present in 38.3% (126/329) of the patients. The 61.7% (203/329) of patients without COVID-19 formed the negative control group. Among individual stroke-related risk factors, only Hispanic ethnicity was significantly associated with COVID-19 (38.1% of patients with COVID-19 vs 20.7% of patients without COVID-19; p = 0.001). LVO was present in 31.7% of patients with COVID-19 compared with 15.3% of patients without COVID-19 (p = 0.001). SVO was present in 15.9% of patients with COVID-19 and 13.8% of patients without COVID-19 (p = 0.632). In multivariate analysis controlled for race and ethnicity, presence of COVID-19 had a significant independent association with LVO stroke (odds ratio, 2.4) compared with absence of COVID-19 (p = 0.011). CONCLUSION. COVID-19 is associated with LVO strokes but not with SVO strokes. CLINICAL IMPACT. Patients with COVID-19 presenting with acute neurologic symptoms warrant a lower threshold for suspicion of large vessel stroke, and prompt workup for large vessel stroke is recommended.

Gadoxetate-enhanced abbreviated MRI is highly accurate for hepatocellular carcinoma screening.

OBJECTIVES: The primary objective was to compare the performance of 3 different abbreviated MRI (AMRI) sets extracted from a complete gadoxetate-enhanced MRI obtained for hepatocellular carcinoma (HCC) screening. Secondary objective was to perform a preliminary cost-effectiveness analysis, comparing each AMRI set to published ultrasound performance for HCC screening in the USA. METHODS: This retrospective study included 237 consecutive patients (M/F, 146/91; mean age, 58 years) with chronic liver disease who underwent a complete gadoxetate-enhanced MRI for HCC screening in 2017 in a single institution. Two radiologists independently reviewed 3 AMRI sets extracted from the complete exam: non-contrast (NC-AMRI: T2-weighted imaging (T2wi)+diffusion-weighted imaging (DWI)), dynamic-AMRI (Dyn-AMRI: T2wi+DWI+dynamic T1wi), and hepatobiliary phase AMRI (HBP-AMRI: T2wi+DWI+T1wi during the HBP). Each patient was classified as HCC-positive/HCC-negative based on the reference standard, which consisted in all available patient data. Diagnostic performance for HCC detection was compared between sets. Estimated set characteristics, including historical ultrasound data, were incorporated into a microsimulation model for cost-effectiveness analysis. RESULTS: The reference standard identified 13/237 patients with HCC (prevalence, 5.5%; mean size, 33.7 ± 30 mm). Pooled sensitivities were 61.5% for NC-AMRI (95% confidence intervals, 34.4-83%), 84.6% for Dyn-AMRI (60.8-95.1%), and 80.8% for HBP-AMRI (53.6-93.9%), without difference between sets (p range, 0.06-0.16). Pooled specificities were 95.5% (92.4-97.4%), 99.8% (98.4-100%), and 94.9% (91.6-96.9%), respectively, with a significant difference between Dyn-AMRI and the other sets (p < 0.01). All AMRI methods were effective compared with ultrasound, with life-year gain of 3-12 months against incremental costs of US$ < 12,000. CONCLUSIONS: NC-AMRI has limited sensitivity for HCC detection, while HBP-AMRI and Dyn-AMRI showed excellent sensitivity and specificity, the latter being slightly higher for Dyn-AMRI. Cost-effectiveness estimates showed that AMRI is effective compared with ultrasound. KEY POINTS: • Comparison of different abbreviated MRI (AMRI) sets reconstructed from a complete gadoxetate MRI demonstrated that non-contrast AMRI has low sensitivity (61.5%) compared with contrast-enhanced AMRI (80.8% for hepatobiliary phase AMRI and 84.6% for dynamic AMRI), with all sets having high specificity. • Non-contrast and hepatobiliary phase AMRI can be performed in less than 14 min (including set-up time), while dynamic AMRI can be performed in less than 17 min. • All AMRI sets were cost-effective for HCC screening in at-risk population in comparison with ultrasound.

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting.

Patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status have been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffused Weighted Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.

Prediction of the histopathologic findings of intrahepatic cholangiocarcinoma: qualitative and quantitative assessment of diffusion-weighted imaging.

OBJECTIVE: To correlate qualitative and quantitative diffusion weighted imaging (DWI) characteristics of intrahepatic cholangiocarcinoma (ICC) with histopathologic tumour grade and fibrosis content. METHODS: Fifty-one patients (21M/30F; mean age 61y) with ICC and MRI including DWI were included in this IRB-approved multicentre retrospective study. Qualitative tumour features were assessed. Tumour apparent diffusion coefficient (ADC) mean, minimum, and normalized (nADCliver) values were computed. Tumour grade [well(G1), moderately(G2), or poorly differentiated(G3)] and tumour fibrosis content [minimal(1), moderate(2), or abundant(3)] were categorized pathologically. Imaging findings and ADC values were compared with pathologic measures. Utility of ADC values for predicting tumour grade was assessed using ROC analysis. RESULTS: 51 ICCs (mean size 6.5±1.1 cm) were assessed. 33/51(64%) of ICCs demonstrated diffuse hyperintensity and 15/51(29%) demonstrated target appearance on DWI. Infiltrative morphology (p=0.02) and tumour size (p=0.04) were associated with G3. ADCmean and nADCmean of G3 (1.32±0.47x10-3 mm2/sec and 0.97±0.95) were lower than G1+G2 (1.57±0.39x10-3 mm2/sec and 1.24±0.49; p=0.03 and p=0.04). ADCmean and nADCmean were inversely correlated with tumour grade (p<0.025). No correlation was found between ADC and tumour fibrosis content. AUROC, sensitivity and specificity of nADCmean for G3 versus G1+G2 were 0.71, 89.5% and 55.5%. CONCLUSION: ADC quantification has reasonable accuracy for predicting ICC grade. KEY POINTS: • ADC quantification was useful for predicting ICC tumour grade. • Infiltrative tumour morphology and size were associated with poorly differentiated ICCs. • ADC values depended more on ICC tumour grade than fibrosis content. • Ability to predict ICC tumour grade non-invasively could impact patient management.

Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation.

Follicular lymphoma (FL) is incurable with conventional therapies and has a clinical course typified by multiple relapses after therapy. These tumors are genetically characterized by B-cell leukemia/lymphoma 2 (BCL2) translocation and mutation of genes involved in chromatin modification. By analyzing purified tumor cells, we identified additional novel recurrently mutated genes and confirmed mutations of one or more chromatin modifier genes within 96% of FL tumors and two or more in 76% of tumors. We defined the hierarchy of somatic mutations arising during tumor evolution by analyzing the phylogenetic relationship of somatic mutations across the coding genomes of 59 sequentially acquired biopsies from 22 patients. Among all somatically mutated genes, CREBBP mutations were most significantly enriched within the earliest inferable progenitor. These mutations were associated with a signature of decreased antigen presentation characterized by reduced transcript and protein abundance of MHC class II on tumor B cells, in line with the role of CREBBP in promoting class II transactivator (CIITA)-dependent transcriptional activation of these genes. CREBBP mutant B cells stimulated less proliferation of T cells in vitro compared with wild-type B cells from the same tumor. Transcriptional signatures of tumor-infiltrating T cells were indicative of reduced proliferation, and this corresponded to decreased frequencies of tumor-infiltrating CD4 helper T cells and CD8 memory cytotoxic T cells. These observations therefore implicate CREBBP mutation as an early event in FL evolution that contributes to immune evasion via decreased antigen presentation.

Evaluation of HCC response to locoregional therapy: Validation of MRI-based response criteria versus explant pathology.

BACKGROUND AND AIMS: This study evaluates the performance of various magnetic resonance imaging (MRI) response criteria for the prediction of complete pathologic necrosis (CPN) of hepatocellular carcinoma (HCC) post locoregional therapy (LRT) using explant pathology as a reference. METHODS: We included 61 patients (male/female 46/15; mean age 60years) who underwent liver transplantation after LRT with transarterial chemoembolization plus radiofrequency or microwave ablation (n=56), or 90Yttrium radioembolization (n=5). MRI was performed <90days before liver transplantation. Three independent readers assessed the following criteria: RECIST, EASL, modified RECIST (mRECIST), percentage of necrosis on subtraction images, and diffusion-weighted imaging (DWI), both qualitative (signal intensity) and quantitative (apparent diffusion coefficient [ADC]). The degree of necrosis was retrospectively assessed at histopathology. Intraclass correlation coefficient (ICC) and Cohen's kappa were used to assess inter-reader agreement. Logistic regression and receiver operating characteristic analyses were used to determine imaging predictors of CPN. Pearson correlation was performed between imaging criteria and pathologic degree of tumor necrosis. RESULTS: A total of 97HCCs (mean size 2.3±1.3cm) including 28 with CPN were evaluated. There was excellent inter-reader agreement (ICC 0.77-0.86, all methods). EASL, mRECIST, percentage of necrosis and qualitative DWI were all significant (p<0.001) predictors of CPN, while RECIST and ADC were not. EASL, mRECIST and percentage of necrosis performed similarly (area under the curves [AUCs] 0.810-0.815) while the performance of qualitative DWI was lower (AUC 0.622). Image subtraction demonstrated the strongest correlation (r=0.71-0.72, p<0.0001) with pathologic degree of tumor necrosis. CONCLUSIONS: EASL/mRECIST criteria and image subtraction have excellent diagnostic performance for predicting CPN in HCC treated with LRT, with image subtraction correlating best with pathologic degree of tumor necrosis. Thus, MR image subtraction is recommended for assessing HCC response to LRT. LAY SUMMARY: The assessment of hepatocellular carcinoma (HCC) tumor necrosis after locoregional therapy is essential for additional treatment planning and estimation of outcome. In this study, we assessed the performance of various magnetic resonance imaging (MRI) response criteria (RECIST, mRECIST, EASL, percentage of necrosis on subtraction images, and diffusion-weighted imaging) for the prediction of complete pathologic necrosis of HCC post locoregional therapy on liver explant. Patients who underwent liver transplantation after locoregional therapy were included in this retrospective study. All patients underwent routine liver MRI within 90days of liver transplantation. EASL/mRECIST criteria and image subtraction had excellent diagnostic performance for predicting complete pathologic necrosis in treated HCC, with image subtraction correlating best with pathologic degree of tumor necrosis.

Noninvasive monitoring of diffuse large B-cell lymphoma by immunoglobulin high-throughput sequencing.

Recent studies have shown limited utility of routine surveillance imaging for diffuse large B-cell lymphoma (DLBCL) patients achieving remission. Detection of molecular disease by immunoglobulin high-throughput sequencing (Ig-HTS) from peripheral blood provides an alternate strategy for surveillance. We prospectively evaluated the utility of Ig-HTS within 311 blood and 105 tumor samples from 75 patients with DLBCL, comparing Ig-HTS from the cellular (circulating leukocytes) and acellular (plasma cell-free DNA) compartments of peripheral blood to clinical outcomes and (18)fluoro-deoxyglucose positron emission tomography combined with computed tomography (PET/CT; n = 173). Clonotypic immunoglobulin rearrangements were detected in 83% of patients with adequate tumor samples to enable subsequent monitoring in peripheral blood. Molecular disease measured from plasma, compared with circulating leukocytes, was more abundant and better correlated with radiographic disease burden. Before treatment, molecular disease was detected in the plasma of 82% of patients compared with 71% in circulating cells (P = .68). However, molecular disease was detected significantly more frequently in the plasma at time of relapse (100% vs 30%; P = .001). Detection of molecular disease in the plasma often preceded PET/CT detection of relapse in patients initially achieving remission. During surveillance time points before relapse, plasma Ig-HTS demonstrated improved specificity (100% vs 56%, P < .0001) and similar sensitivity (31% vs 55%, P = .4) compared with PET/CT. Given its high specificity, Ig-HTS from plasma has potential clinical utility for surveillance after complete remission.

Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results.

OBJECTIVES: In this preliminary study, we examined whether imaging-based phenotypes are associated with reported predictive gene signatures in hepatocellular carcinoma (HCC). METHODS: Thirty-eight patients (M/F 30/8, mean age 61 years) who underwent pre-operative CT or MR imaging before surgery as well as transcriptome profiling were included in this IRB-approved single-centre retrospective study. Eleven qualitative and four quantitative imaging traits (size, enhancement ratios, wash-out ratio, tumour-to-liver contrast ratios) were assessed by three observers and were correlated with 13 previously reported HCC gene signatures using logistic regression analysis. RESULTS: Thirty-nine HCC tumours (mean size 5.7 ± 3.2 cm) were assessed. Significant positive associations were observed between certain imaging traits and gene signatures of aggressive HCC phenotype (G3-Boyault, Proliferation-Chiang profiles, CK19-Villanueva, S1/S2-Hoshida) with odds ratios ranging from 4.44-12.73 (P <0.045). Infiltrative pattern at imaging was significantly associated with signatures of microvascular invasion and aggressive phenotype. Significant but weak associations were also observed between each enhancement ratio and tumour-to-liver contrast ratios and certain gene expression profiles. CONCLUSIONS: This preliminary study demonstrates a correlation between phenotypic imaging traits with gene signatures of aggressive HCC, which warrants further prospective validation to establish imaging-based surrogate markers of molecular phenotypes in HCC. KEY POINTS: • There are associations between imaging and gene signatures of aggressive hepatocellular carcinoma. • Infiltrative type is associated with gene signatures of microvascular invasion and aggressiveness. • Infiltrative type may be a surrogate marker of microvascular invasion gene signature.

Hierarchy in somatic mutations arising during genomic evolution and progression of follicular lymphoma.

Follicular lymphoma (FL) is currently incurable using conventional chemotherapy or immunotherapy regimes, compelling new strategies. Advances in high-throughput sequencing technologies that can reveal oncogenic pathways have stimulated interest in tailoring therapies toward actionable somatic mutations. However, for mutation-directed therapies to be most effective, the mutations must be uniformly present in evolved tumor cells as well as in the self-renewing tumor-cell precursors. Here, we show striking intratumoral clonal diversity within FL tumors in the representation of mutations in the majority of genes as revealed by whole exome sequencing of subpopulations. This diversity captures a clonal hierarchy, resolved using immunoglobulin somatic mutations and IGH-BCL2 translocations as a frame of reference and by comparing diagnosis and relapse tumor pairs, allowing us to distinguish early versus late genetic eventsduring lymphomagenesis. We provide evidence that IGH-BCL2 translocations and CREBBP mutations are early events, whereas MLL2 and TNFRSF14 mutations probably represent late events during disease evolution. These observations provide insight into which of the genetic lesions represent suitable candidates for targeted therapies.

Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.

BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.

Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign.

PURPOSE: The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. METHODS: In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. RESULTS: A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. CONCLUSION: The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.

Neuroimaging findings and neurological manifestations in hospitalized COVID-19 patients: Impact of cancer and ventilatory support status.

INTRODUCTION: Coronavirus 2019 (COVID-19) is known to affect the central nervous system. Neurologic morbidity associated with COVID-19 is commonly attributed to sequelae of some combination of thrombotic and inflammatory processes. The aim of this retrospective observational study was to evaluate neuroimaging findings in hospitalized COVID-19 patients with neurological manifestations in cancer versus non-cancer patients, and in patients with versus without ventilatory support (with ventilatory support defined as including patients with intubation and noninvasive ventilation). Cancer patients are frequently in an immunocompromised or prothrombotic state with side effects from chemotherapy and radiation that may cause neurological issues and increase vulnerability to systemic illness. We wanted to determine whether neurological and/or neuroimaging findings differed between patients with and without cancer. METHODS: Eighty adults (44 male, 36 female, 64.5 ±14 years) hospitalized in the Mount Sinai Health System in New York City between March 2020 and April 2021 with reverse-transcriptase polymerase chain reaction-confirmed COVID-19 underwent magnetic resonance imaging (MRI) during their admissions. The cohort consisted of four equal subgroups based on cancer and ventilatory support status. Clinical and imaging data were acquired and analyzed. RESULTS: Neuroimaging findings included non-ischemic parenchymal T2/FLAIR signal hyperintensities (36.3%), acute/subacute infarcts (26.3%), chronic infarcts (25.0%), microhemorrhages (23.8%), chronic macrohemorrhages (10.0%), acute macrohemorrhages (7.5%), and encephalitis-like findings (7.5%). There were no significant differences in neuroimaging findings between cancer and non-cancer subgroups. Clinical neurological manifestations varied. The most common was encephalopathy (77.5%), followed by impaired responsiveness/coma (38.8%) and stroke (26.3%). There were significant differences between patients with versus without ventilatory support. Encephalopathy and impaired responsiveness/coma were more prevalent in patients with ventilatory support (p = 0.02). Focal weakness was more frequently seen in patients without ventilatory support (p = 0.01). DISCUSSION: This study suggests COVID-19 is associated with neurological manifestations that may be visible with brain imaging techniques such as MRI. In our COVID-19 cohort, there was no association between cancer status and neuroimaging findings. Future studies might include more prospectively enrolled systematically characterized patients, allowing for more rigorous statistical analysis.

Perfusion Collateral Index versus Hypoperfusion Intensity Ratio in Assessment of Collaterals in Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Perfusion-based collateral indices such as the perfusion collateral index and the hypoperfusion intensity ratio have shown promise in the assessment of collaterals in patients with acute ischemic stroke. We aimed to compare the diagnostic performance of the perfusion collateral index and the hypoperfusion intensity ratio in collateral assessment compared with angiographic collaterals and outcome measures, including final infarct volume, infarct growth, and functional independence. MATERIALS AND METHODS: Consecutive patients with acute ischemic stroke with anterior circulation proximal arterial occlusion who underwent endovascular thrombectomy and had pre- and posttreatment MRI were included. Using pretreatment MR perfusion, we calculated the perfusion collateral index and the hypoperfusion intensity ratio for each patient. The angiographic collaterals obtained from DSA were dichotomized to sufficient (American Society of Interventional and Therapeutic Neuroradiology [ASITN] scale 3-4) versus insufficient (ASITN scale 0-2). The association of collateral status determined by the perfusion collateral index and the hypoperfusion intensity ratio was assessed against angiographic collaterals and outcome measures. RESULTS: A total of 98 patients met the inclusion criteria. Perfusion collateral index values were significantly higher in patients with sufficient angiographic collaterals (P < .001), while there was no significant (P = .46) difference in hypoperfusion intensity ratio values. Among patients with good (mRS 0-2) versus poor (mRS 3-6) functional outcome, the perfusion collateral index of ≥ 62 was present in 72% versus 31% (P = .003), while the hypoperfusion intensity ratio of ≤0.4 was present in 69% versus 56% (P = .52). The perfusion collateral index and the hypoperfusion intensity ratio were both significantly predictive of final infarct volume, but only the perfusion collateral index was significantly (P = .03) associated with infarct growth. CONCLUSIONS: Results show that the perfusion collateral index outperforms the hypoperfusion intensity ratio in the assessment of collateral status, infarct growth, and determination of functional outcomes.

Automated assessment of ischemic core on non-contrast computed tomography: a multicenter comparative analysis with CT perfusion.

BACKGROUND: Application of machine learning (ML) algorithms has shown promising results in estimating ischemic core volumes using non-contrast CT (NCCT). OBJECTIVE: To assess the performance of the e-Stroke Suite software (Brainomix) in assessing ischemic core volumes on NCCT compared with CT perfusion (CTP) in patients with acute ischemic stroke. METHODS: In this retrospective multicenter study, patients with anterior circulation large vessel occlusions who underwent pretreatment NCCT and CTP, successful reperfusion (modified Thrombolysis in Cerbral Infarction ≥2b), and post-treatment MRI, were included from three stroke centers. Automated calculation of ischemic core volumes was obtained on NCCT scans using ML algorithm deployed by e-Stroke Suite and from CTP using Olea software (Olea Medical). Comparative analysis was performed between estimated core volumes on NCCT and CTP and against MRI calculated final infarct volume (FIV). RESULTS: A total of 111 patients were included. Estimated ischemic core volumes (mean±SD, mL) were 20.4±19.0 on NCCT and 19.9±18.6 on CTP, not significantly different (P=0.82). There was moderate (r=0.40) and significant (P<0.001) correlation between estimated core on NCCT and CTP. The mean difference between FIV and estimated core volume on NCCT and CTP was 29.9±34.6 mL and 29.6±35.0 mL, respectively (P=0.94). Correlations between FIV and estimated core volume were similar for NCCT (r=0.30, P=0.001) and CTP (r=0.36, P<0.001). CONCLUSIONS: Results show that ML-based estimated ischemic core volumes on NCCT are comparable to those obtained from concurrent CTP in magnitude and in degree of correlation with MR-assessed FIV.

U-Net Based Segmentation and Characterization of Gliomas.

(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40−50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of <18 months demonstrated AUC of 0.75. (4) Conclusions: Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.

Trans-synaptic degeneration of the optic radiation from optic nerve atrophy.

Fourty-seven-year-old woman with 5-year history of progressive decreased left eye vision. Optical coherence tomography showed optic nerve atrophy (left > right) and brain MRI revealed T2 hyperintense signal along the course of left optic radiations. We present a case of a trans-synaptic degeneration of the optic radiation in a patient with confirmed optic atrophy. Trans-synaptic degeneration of the optic radiation without associated infarct or inflammatory disease has not been reported before in patients with optic atrophy.

Infundibulum at the origin of an accessory middle cerebral artery.

We present a case of an infundibular dilation at the origin of an accessory middle cerebral artery emanating from the distal A1 segment of the anterior cerebral artery. There was also partial vessel wall enhancement along this infundibulum. To our knowledge, this is the first case report with such findings.

Neurovascular complications that can be seen in COVID-19 patients.

Coronavirus disease 2019 (COVID-19), a clinical manifestation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), was declared a global pandemic by the World Health Organization on March 11, 2020. Hypercoagulable state has been described as one of the hallmarks of SARS-CoV-2 infection and has been reported to manifest as pulmonary embolisms, deep vein thrombosis, and arterial thrombosis of the abdominal small vessels. Here we present cases of arterial and venous thrombosis pertaining to the head and neck in COVID-19 patients.

Sources of Revenue Loss and Recovery in Radiology Practices During the Coronavirus Disease 2019 (COVID-19) Pandemic.

RATIONALE AND OBJECTIVES: This study seeks to quantify the financial impact of COVID-19 on radiology departments, and to describe the structure of both volume and revenue recovery. MATERIALS AND METHODS: Radiology studies from a large academic health system were retrospectively studied from the first 33 weeks of 2020. Volume and work relative value unit (wRVU) data were aggregated on a weekly basis for three periods: Presurge (weeks 1-9), surge (10-19), and recovery (20-33), and analyzed compared to the pre-COVID baseline stratified by modality, specialty, patient service location, and facility type. Mean and median wRVU per study were used as a surrogate for case complexity. RESULTS: During the pandemic surge, case volumes fell 57%, while wRVUs fell by 69% relative to the pre-COVID-19 baseline. Mean wRVU per study was 1.13 in the presurge period, 1.03 during the surge, and 1.19 in the recovery. Categories with the greatest mean complexity declines were radiography (-14.7%), cardiothoracic imaging (-16.2%), and community hospitals overall (-15.9%). Breast imaging (+6.5%), interventional (+5.5%), and outpatient (+12.1%) complexity increased. During the recovery, significant increases in complexity were seen in cardiothoracic (0.46 to 0.49), abdominal (1.80 to 1.91), and neuroradiology (2.46 to 2.56) at stand-alone outpatient centers with similar changes at community hospitals. At academic hospitals, only breast imaging complexity remained elevated (1.32 from 1.17) during the recovery. CONCLUSION: Reliance on volume alone underestimates the financial impact of the COVID-19 pandemic as there was a disproportionate loss in high-RVU studies. However, increased complexity of outpatient cases has stabilized overall losses during the recovery.