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Diagnostic performance of lung ultrasound compared to CT scan in the diagnosis of pulmonary lesions of COVID-19 induced pneumonia: a preliminary study.

ABSTRACT: Chest CT scan is currently used to assess the extent of lung involvement in patients with the coronavirus disease 2019 (COVID-19). The aim of this study was to evaluate the diagnostic performance of lung ultrasound in the diagnosis of COVID-19 pulmonary manifestations in comparison to CT scan. Thirty-three symptomatic patients with suspected COVID-19 pneumonia were evaluated by lung ultrasound and then, at a short interval, chest CT scan. In the anterior chest, each hemithorax was divided into four areas. In the posterior chest, eight zones similar to the anterior part were examined. The axillary areas were also divided into upper and lower zones (20 zones were determined per patient). Mean age of the patients was 58.66 years. The sensitivity (95% CI) and specificity (95% CI) of lung ultrasound for the diagnosis of parenchymal lesions were 90.5% (69.6-98.8%) and 50% (21.1-78.9%), respectively. In the evaluation of pleural lesions, the sensitivity (95% CI) and specificity (95% CI) of lung ultrasound were 100% (71.5-100%) and 22.7% (7.8-45.4%), respectively. Owing to the high sensitivity of ultrasound in identifying lung lesions in patients with COVID-19 pneumonia, it can be recommended to use lung ultrasound as a tool for initial screening of patients with high clinical suspicion for SARS-CoV-2 infection during the pandemic.

Supplementary information

The online version contains supplementary material available at 10.1007/s13337-021-00736-w.

SUBMITTER: Roshandel J

PROVIDER: S-EPMC8372226 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLimited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model.MethodsThis study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).ResultsEight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model.ConclusionsThe combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

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Diagnostic performance of lung ultrasound compared to CT scan in the diagnosis of pulmonary lesions of COVID-19 induced pneumonia: a preliminary study.

Supplementary information

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