A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

PURPOSE: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction. METHOD: We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction. RESULTS: For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort. CONCLUSION: The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Anterior Insular Resting-State Functional Connectivity is Related to Cognitive Reserve in Multiple Sclerosis.

BACKGROUND AND PURPOSE: Cognitive dysfunction is common in multiple sclerosis (MS). The dorsal anterior insula (dAI) is a key hub of the salience network (SN) orchestrating access to critical cognitive brain regions. The aim of this study was to assess whole-brain dAI intrinsic functional connectivity (iFC) using resting-state functional MRI (rs-fMRI) in people with MS and healthy controls (HC) and test the relationship between cognitive reserve (CR) and dAI iFC in people with MS. METHODS: We studied 28 people with relapsing-remitting MS and 28 HC. CR index was quantified by combining premorbid IQ, leisure activities, and education level. For whole-brain iFC analyses, the bilateral dAI were used as seeds. Individual subject correlation maps were entered into general linear models for group comparison and to analyze the effect of CR index on dAI iFC, controlling for multiple comparisons. The correlation between CR index and iFC was assessed using a linear regression model. RESULTS: rs-fMRI analyses revealed a negative relationship between CR index and iFC within the left dAI and a left occipital cluster in people with MS including regions of the cuneus, superior occipital gyrus, and parieto-occipital sulcus. The regression analysis showed that people with MS and a higher CR index had a statistically significantly reduced iFC within the left dAI and the cluster. CONCLUSIONS: CR is relevant to functional connectivity within one of the main nodes of the SN, the dAI, and occipital regions in MS. These results have implications for how CR may modulate the susceptibility to cognitive dysfunction in MS.

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study.

PURPOSE: To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi. METHODS: The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output. RESULTS: Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85-0.92). Higher-order radiomics (gray-level size zone matrix - GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89-0.92). CONCLUSION: Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.

CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia.

PURPOSE: To compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables. MATERIALS AND METHODS: This institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients, n = 210; outpatients, n = 105) were followed-up for at least 2 weeks to record disease outcome. Clinical variables, such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases, were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. Radiomics were obtained for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as outputs were performed. RESULTS: Most patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretations of disease extent and opacity type had an AUC of 0.69 (P < .0001). Whole lung radiomics were superior to the radiologists' interpretation for predicting patient outcome in terms of intensive care unit (ICU) admission (AUC: 0.75 vs 0.68) and death (AUC: 0.81 vs 0.68) (P < .002). The addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission. CONCLUSION: Radiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.

Cortical Thickness and Episodic Memory Impairment in Systemic Lupus Erythematosus.

BACKGROUND AND PURPOSE: The purpose of this study was to investigate differences in brain cortical thickness of systemic lupus erythematosus (SLE) patients with and without episodic memory impairment and healthy controls. METHODS: We studied 51 patients divided in 2 groups (SLE with episodic memory deficit, n = 17; SLE without episodic memory deficit, n = 34) by the Rey Auditory Verbal Learning Test and 34 healthy controls. Groups were paired based on sex, age, education, Mini-Mental State Examination score, and accumulation of disease burden. Cortical thickness from magnetic resonance imaging scans was determined using the FreeSurfer software package. RESULTS: SLE patients with episodic memory deficits presented reduced cortical thickness in the left supramarginal cortex and superior temporal gyrus when compared to the control group and in the right superior frontal, caudal, and rostral middle frontal and precentral gyri when compared to the SLE group without episodic memory impairment considering time since diagnosis of SLE as covaried. There were no significant differences in the cortical thickness between the SLE without episodic memory and control groups. CONCLUSIONS: Different memory-related cortical regions thinning were found in the episodic memory deficit group when individually compared to the groups of patients without memory impairment and healthy controls.