Pulmonary Vascular Manifestations of COVID-19 Pneumonia.

PURPOSE: To investigate pulmonary vascular abnormalities at CT pulmonary angiography (CT-PE) in patients with coronavirus disease 2019 (COVID-19) pneumonia. MATERIALS AND METHODS: In this retrospective study, 48 patients with reverse-transcription polymerase chain reaction-confirmed COVID-19 infection who had undergone CT-PE between March 23 and April 6, 2020, in a large urban health care system were included. Patient demographics and clinical data were collected through the electronic medical record system. Twenty-five patients underwent dual-energy CT (DECT) as part of the standard CT-PE protocol at a subset of the hospitals. Two thoracic radiologists independently assessed all studies. Disagreement in assessment was resolved by consensus discussion with a third thoracic radiologist. RESULTS: Of the 48 patients, 45 patients required admission, with 18 admitted to the intensive care unit, and 13 requiring intubation. Seven patients (15%) were found to have pulmonary emboli. Dilated vessels were seen in 41 cases (85%), with 38 (78%) and 27 (55%) cases demonstrating vessel enlargement within and outside of lung opacities, respectively. Dilated distal vessels extending to the pleura and fissures were seen in 40 cases (82%) and 30 cases (61%), respectively. At DECT, mosaic perfusion pattern was observed in 24 cases (96%), regional hyperemia overlapping with areas of pulmonary opacities or immediately surrounding the opacities were seen in 13 cases (52%), opacities associated with corresponding oligemia were seen in 24 cases (96%), and hyperemic halo was seen in 9 cases (36%). CONCLUSION: Pulmonary vascular abnormalities such as vessel enlargement and regional mosaic perfusion patterns are common in COVID-19 pneumonia. Perfusion abnormalities are also frequently observed at DECT in COVID-19 pneumonia and may suggest an underlying vascular process.Supplemental material is available for this article.© RSNA, 2020.

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.

Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.

RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement. MATERIALS AND METHODS: We performed a retrospective multi-radiologist user study to evaluate the impact of an AI system, the PXS score model, on the grading of categorical COVID-19 lung disease severity on 154 chest radiographs into four ordinal grades (normal/minimal, mild, moderate, and severe). Four radiologists (two thoracic and two emergency radiologists) independently interpreted 154 CXRs from 154 unique patients with COVID-19 hospitalized at a large academic center, before and after using the AI system (median washout time interval was 16 days). Three different thoracic radiologists assessed the same 154 CXRs using an updated version of the AI system trained on more imaging data. Radiologist interrater agreement was evaluated using Cohen and Fleiss kappa where appropriate. The lung disease severity categories were associated with clinical outcomes using a previously published outcomes dataset using Fisher's exact test and Chi-square test for trend. RESULTS: Use of the AI system improved radiologist interrater agreement (Fleiss κ = 0.40 to 0.66, before and after use of the system). The Fleiss κ for three radiologists using the updated AI system was 0.74. Severity categories were significantly associated with subsequent intubation or death within 3 days. CONCLUSION: An AI system used at the time of CXR study interpretation can improve the interrater agreement of radiologists.

Detection of Unsuspected Coronavirus Disease 2019 Cases by Computed Tomography and Retrospective Implementation of the Radiological Society of North America/Society of Thoracic Radiology/American College of Radiology Consensus Guidelines.

PURPOSE: The purpose of this article was to report the utility of computed tomography (CT) for detecting unsuspected cases of Coronavirus disease 2019 (COVID-19) and the utility of the Radiological Society of North America (RSNA)/Society of Thoracic Radiology (STR)/American College of Radiology (ACR) consensus guidelines for COVID-19 reporting. MATERIALS AND METHODS: A total of 22 patients of the 156 reverse transcriptase polymerase chain reaction confirmed COVID-19 patients who were hospitalized between March 27, 2020 and March 31, 2020 at our quaternary care academic medical center and who underwent CT imaging within 1 week of admission were included in this retrospective study. Demographics and clinical data were extracted from the electronic medical record system. Two thoracic radiologists independently categorized each CT study on the basis of RSNA/STR/ACR consensus guidelines. Disagreement in categorization was resolved by consensus discussion with a third thoracic radiologist. RESULTS: At the time of imaging, 16 patients (73%) were suspected of COVID-19, and 6 patients (27%) were not. Common symptoms at presentation were fever (73%), cough (77%), and gastrointestinal symptoms (59%). An overall 63% of suspected COVID-19 patients exhibited shortness of breath, whereas 0 unsuspected COVID-19 patients did (P=0.02). On the basis of the RSNA consensus guidelines, 68%, 18%, 9%, and 5% of studies were categorized as "typical appearance," "indeterminate appearance," "atypical appearance," and "negative for pneumonia," respectively. There was no difference of category distribution between suspected and unsuspected COVID-19 patients (P=0.20), with "typical appearance" being the most prevalent in both (69% vs. 67%, respectively). CONCLUSIONS: It is important to recognize imaging features of COVID-19 pneumonia even in unsuspected patients. Implementation of the RSNA/STR/ACR consensus guidelines may increase consistency of reporting and convey the level of suspicion for COVID-19 to other health care providers, with "typical appearance" especially warranting further attention.