Safety and immunogenicity of COVID-19 vaccination in patients with hepatocellular carcinoma (CHESS-NMCID 2101): A multicenter prospective study.

Data on safety and immunogenicity of coronavirus disease 2019 (COVID-19) vaccinations in hepatocellular carcinoma (HCC) patients are limited. In this multicenter prospective study, HCC patients received two doses of inactivated whole-virion COVID-19 vaccines. The safety and neutralizing antibody were monitored. Totally, 74 patients were enrolled from 10 centers in China, and 37 (50.0%), 25 (33.8%), and 12 (16.2%) received the CoronaVac, BBIBP-CorV, and WIBP-CorV, respectively. The vaccines were well tolerated, where pain at the injection site (6.8% [5/74]) and anorexia (2.7% [2/74]) were the most frequent local and systemic adverse events. The median level of neutralizing antibody was 13.5 (interquartile range [IQR]: 6.9-23.2) AU/ml at 45 (IQR: 19-72) days after the second dose of vaccinations, and 60.8% (45/74) of patients had positive neutralizing antibody. Additionally, lower γ-glutamyl transpeptidase level was related to positive neutralizing antibody (odds ratio = 1.022 [1.003-1.049], p = 0.049). In conclusion, this study found that inactivated COVID-19 vaccinations are safe and the immunogenicity is acceptable or hyporesponsive in patients with HCC. Given that the potential benefits may outweigh the risks and the continuing emergences of novel severe acute respiratory syndrome coronavirus 2 variants, we suggest HCC patients to be vaccinated against COVID-19. Future validation studies are warranted.

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.

Safety and immunogenicity of COVID-19 vaccination in patients with non-alcoholic fatty liver disease (CHESS2101): A multicenter study.

BACKGROUND & AIMS: The development of COVID-19 vaccines has progressed with encouraging safety and efficacy data. Concerns have been raised about SARS-CoV-2 vaccine responses in the large population of patients with non-alcoholic fatty liver disease (NAFLD). The study aimed to explore the safety and immunogenicity of COVID-19 vaccination in NAFLD. METHODS: This multicenter study included patients with NAFLD without a history of SARS-CoV-2 infection. All patients were vaccinated with 2 doses of inactivated vaccine against SARS-CoV-2. The primary safety outcome was the incidence of adverse reactions within 7 days after each injection and overall incidence of adverse reactions within 28 days, and the primary immunogenicity outcome was neutralizing antibody response at least 14 days after the whole-course vaccination. RESULTS: A total of 381 patients with pre-existing NAFLD were included from 11 designated centers in China. The median age was 39.0 years (IQR 33.0-48.0 years) and 179 (47.0%) were male. The median BMI was 26.1 kg/m2 (IQR 23.8-28.1 kg/m2). The number of adverse reactions within 7 days after each injection and adverse reactions within 28 days totaled 95 (24.9%) and 112 (29.4%), respectively. The most common adverse reactions were injection site pain in 70 (18.4%), followed by muscle pain in 21 (5.5%), and headache in 20 (5.2%). All adverse reactions were mild and self-limiting, and no grade 3 adverse reactions were recorded. Notably, neutralizing antibodies against SARS-CoV-2 were detected in 364 (95.5%) patients with NAFLD. The median neutralizing antibody titer was 32 (IQR 8-64), and the neutralizing antibody titers were maintained. CONCLUSIONS: The inactivated COVID-19 vaccine appears to be safe with good immunogenicity in patients with NAFLD. LAY SUMMARY: The development of vaccines against coronavirus disease 2019 (COVID-19) has progressed rapidly, with encouraging safety and efficacy data. This study now shows that the inactivated COVID-19 vaccine appears to be safe with good immunogenicity in the large population of patients with non-alcoholic fatty liver disease.

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.