Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis. Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model. Results: 801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes. Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome. Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.

Comparison of Chest CT Findings of COVID-19, Influenza, and Organizing Pneumonia: A Multireader Study.

BACKGROUND. Previous studies compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies to date have included noninfectious organizing pneumonia (OP) for comparison. OBJECTIVE. The objectives of this study were to compare chest CT features of COVID-19, influenza, and OP using a multireader design and to assess the performance of radiologists in distinguishing between these conditions. METHODS. This retrospective study included 150 chest CT examinations in 150 patients (mean [± SD] age, 58 ± 16 years) with a diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and for Radiological Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. The CT characteristics of the three diagnoses were compared using random-effects models; the diagnostic performance of the readers was assessed. RESULTS. COVID-19 pneumonia was significantly different (p < .05) from influenza pneumonia for seven of 14 chest CT findings, although it was different (p < .05) from OP for four of 14 findings (central or diffuse distribution was seen in 10% and 7% of COVID-19 cases, respectively, vs 20% and 21% of OP cases, respectively; unilateral distribution was seen in 1% of COVID-19 cases vs 7% of OP cases; non-tree-in-bud nodules was seen in 32% of COVID-19 cases vs 53% of OP cases; tree-in-bud nodules were seen in 6% of COVID-19 cases vs 14% of OP cases). A total of 70% of cases of COVID-19, 33% of influenza cases, and 47% of OP cases had typical findings according to RSNA COVID-19 category assessment (p < .001). The mean percentage of correct favored diagnoses compared with actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. The mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. CONCLUSION. CT findings of COVID-19 substantially overlap with those of influenza and, to a greater extent, those of OP. The diagnostic accuracy of the radiologists was low in a study sample that contained equal proportions of these three types of pneumonia. CLINICAL IMPACT. Recognized challenges in diagnosing COVID-19 by CT are furthered by the strong overlap observed between the appearances of COVID-19 and OP on CT. This challenge may be particularly evident in clinical settings in which there are substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.

Imaging Volume Trends and Recovery During the COVID-19 Pandemic: A Comparative Analysis Between a Large Urban Academic Hospital and Its Affiliated Imaging Centers.

RATIONALE AND OBJECTIVES: While affiliated imaging centers play an important role in healthcare systems, little is known of how their operations are impacted by the COVID-19 pandemic. Our goal was to investigate imaging volume trends during the pandemic at our large academic hospital compared to the affiliated imaging centers. MATERIALS AND METHODS: This was a descriptive retrospective study of imaging volume from an academic hospital (main hospital campus) and its affiliated imaging centers from January 1 through May 21, 2020. Imaging volume assessment was separated into prestate of emergency (SOE) period (before SOE in Massachusetts on March 10, 2020), "post-SOE" period (time after "nonessential" services closure on March 24, 2020), and "transition" period (between pre-SOE and post-SOE). RESULTS: Imaging volume began to decrease on March 11, 2020, after hospital policy to delay nonessential studies. The average weekly imaging volume during the post-SOE period declined by 54% at the main hospital campus and 64% at the affiliated imaging centers. The rate of imaging volume recovery was slower for affiliated imaging centers (slope = 6.95 for weekdays) compared to main hospital campus (slope = 7.18 for weekdays). CT, radiography, and ultrasound exhibited the lowest volume loss, with weekly volume decrease of 41%, 49%, and 53%, respectively, at the main hospital campus, and 43%, 61%, and 60%, respectively, at affiliated imaging centers. Mammography had the greatest volume loss of 92% at both the main hospital campus and affiliated imaging centers. CONCLUSION: Affiliated imaging center volume decreased to a greater degree than the main hospital campus and showed a slower rate of recovery. Furthermore, the trend in imaging volume and recovery were temporally related to public health announcements and COVID-19 cases.

Implementation of the Radiological Society of North America Expert Consensus Guidelines on Reporting Chest CT Findings Related to COVID-19: A Multireader Performance Study.

BACKGROUND: RSNA expert consensus guidelines provide a framework for reporting CT findings related to COVID-19, but have had limited multireader validation. PURPOSE: To assess the performance of the RSNA guidelines and quantify interobserver variability in application of the guidelines in patients undergoing chest CT for suspected COVID-19 pneumonia. MATERIALS AND METHODS: A retrospective search from 1/15/20 to 3/30/20 identified 89 consecutive CT scans whose radiological report mentioned COVID-19. One positive or two negative RT-PCR tests for COVID-19 were considered the gold standard for diagnosis. Each chest CT scan was evaluated using RSNA guidelines by 9 readers (6 fellowship trained thoracic radiologists and 3 radiology resident trainees). Clinical information was obtained from the electronic medical record. RESULTS: There was strong concordance of findings between radiology training levels with agreement ranging from 60 to 86% among attendings and trainees (kappa 0.43 to 0.86). Sensitivity and specificity of "typical" CT findings for COVID-19 per the RSNA guidelines were on average 86% (range 72%-94%) and 80.2% (range 75-93%), respectively. Combined "typical" and "indeterminate" findings had a sensitivity of 97.5% (range 94-100%) and specificity of 54.7% (range 37-62%). A total of 163 disagreements were seen out of 801 observations (79.6% total agreement). Uncertainty in classification primarily derived from difficulty in ascertaining peripheral distribution, multiple dominant disease processes, or minimal disease. CONCLUSION: The "typical appearance" category for COVID-19 CT reporting has an average sensitivity of 86% and specificity rate of 80%. There is reasonable interreader agreement and good reproducibility across various levels of experience.

Longitudinal assessment of ultrasound-guided complementary microRNA therapy of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the second-leading cause of cancer related deaths worldwide and new strategies to efficiently treat HCC are critically needed. The aim of this study was to assess the longitudinal treatment effects of two complementary miRNAs (miRNA-122 and antimiR-21) encapsulated in biodegradable poly lactic-co-glycolic acid (PLGA) - poly ethylene glycol (PEG) nanoparticles (PLGA-PEG-NPs), administered by an ultrasound-guided and microbubble-mediated delivery approach in doxorubicin-resistant and non-resistant human HCC xenografts. Using in vitro assays, we show that repeated miRNA treatments resulted in gradual reduction of HCC cell proliferation and reversal of doxorubicin resistance. Optimized US parameters resulted in a 9-16 fold increase (p = 0.03) in miRNA delivery in vivo in HCC tumors after two US treatments compared to tumors without US treatment. Furthermore, when combined with doxorubicin (10 mg/kg), longitudinal miRNA delivery showed a significant inhibition of tumor growth in both resistant and non-resistant tumors compared to non-treated, and doxorubicin treated controls. We also found that ultrasound-guided miRNA therapy was not only effective in inhibiting HCC tumor growth but also allowed lowering the dose of doxorubicin needed to induce apoptosis. In conclusion, the results of this study suggest that ultrasound-guided and MB-mediated delivery of miRNA-122 and antimiR-21, when combined with doxorubicin, is a highly effective approach to treat resistant HCC while reducing doxorubicin doses needed for treating non-resistant HCC in longitudinal treatment experiments. Further refinement of this strategy could potentially lead to better treatment outcomes for patients with HCC.