Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study.

OBJECTIVE: We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images. METHODS: This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115). RESULTS: In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035). CONCLUSIONS: Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.

Lopinavir-ritonavir alone or combined with arbidol in the treatment of 73 hospitalized patients with COVID-19: A pilot retrospective study.

OBJECTIVES: This study aimed to evaluate the antiviral efficacy of lopinavir-ritonavir alone or combined with arbidol in the treatment of hospitalized patients with common coronavirus disease-19 (COVID-19). MATERIALS AND METHODS: In this retrospective observational study, hospitalized COVID-19 patients were identified and divided into two groups based on the antiviral agents during their hospitalization. Patients in group LR were treated with lopinavir-ritonavir 400 mg/100 mg, twice a day, while patients in group LR+Ar were treated with lopinavir-ritonavir 400 mg/100 mg twice a day and arbidol 200 mg three times a day for at least 3 days. Data from these patients were collected from electronic medical record management system. RESULTS: 73 patients were divided into two groups: group LR (34 cases) and group LR+Ar (39 cases), according to the antiviral agents. The overall cure rate of COVID-19 in group LR+Ar and group LR were 92.3% and 97.1%, respectively, with no significant difference (p = 0.62). In a modified intention-to-treat analysis, lopinavir-ritonavir combined with arbidol led to a median time of hospital stay that was shorter by 1.5 days than in group LR (12.5 days vs. 14 days). The percentages of -COVID-19 RNA clearance was 92.3 in group LR and 97.1 in group LR+Ar which was similar to the cure rate. The median time to nucleic acid turning negative = (date of first negative PCR test) - (date of last positive PCR test) was 8.0 days in both groups with no significant difference (p = 0.59). Treatment of lopinavir-ritonavir combined with arbidol did not significantly accelerate main symptom improvement and promote the image absorption of pulmonary inflammation. CONCLUSION: No benefit was observed in the antiviral effect of lopinavir-ritonavir combined with arbidol compared with lopinavir-ritonavir alone in the hospitalized patients with COVID-19. More clinical observations in COVID-19 patients may help to confirm or exclude the effect of antiviral agents.

Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a global challenge since the December 2019. The hospital stay is one of the prognostic indicators, and its predicting model based on CT radiomics features is important for assessing the patients' clinical outcome. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with COVID-19 pneumonia. METHODS: This retrospective, multicenter study enrolled patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images from 5 designated hospitals in Ankang, Lishui, Lanzhou, Linxia, and Zhenjiang between January 23, 2020 and February 8, 2020. Patients were classified into short-term (≤10 days) and long-term hospital stay (>10 days). CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features from pneumonia lesions in first four centers. The predictive performance was evaluated in fifth center (test dataset) on lung lobe- and patients-level. RESULTS: A total of 52 patients were enrolled from designated hospitals. As of February 20, 21 patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in analysis. The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with COVID-19 pneumonia, with areas under the curves of 0.97 (95% CI, 0.83-1.0) and 0.92 (95% CI, 0.67-1.0) by LR and RF, respectively, in test. The LR and RF model showed a sensitivity and specificity of 1.0 and 0.89, 0.75 and 1.0 in test respectively. As of February 28, a prospective cohort of six discharged patients were all correctly recognized as long-term stay using RF and LR models. CONCLUSIONS: The machine learning-based CT radiomics features and models showed feasibility and accuracy for predicting hospital stay in patients with COVID-19 pneumonia.