Value of Radiomics Features From Adrenal Gland and Periadrenal Fat in CT Images for Predicting COVID-19 Prognosis (preprint)

Background: Endocrine system plays an important role in infectious disease prognosis. Our goal is to assess the value of radiomics features extracted from adrenal gland and periadrenal fat CT images in predicting disease prognosis in patients with COVID-19. Methods: : A total of 1,325 patients (765 moderate and 560 severe patients) from three centers were enrolled in the retrospective study. We proposed a 3D cascade V-Net to automatically segment adrenal glands in onset CT images. Periadrenal fat areas were obtained using inflation operations. Then, the radiomics features were automatically extracted. Five models were established to predict the disease prognosis in patients with COVID-19: a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], fusion of adrenal gland and periadrenal fat model [FM]), and a radiomics nomogram model (RN).Data from one center (1,183 patients) were utilized as training and validation sets. The remaining two (36 and 106 patients) were used as 2 independent test sets to evaluate the models’ performance. Results: : The auto-segmentation framework achieved an average dice of 0.79 in the test set. CM, AM, PM, FM, and RN obtained AUCs of 0.716, 0.755, 0.796, 0.828, and 0.825, respectively in the training set, and the mean AUCs of 0.754, 0.709, 0.672, 0.706 and 0.778 for 2 independent test sets. Decision curve analysis showed that if the threshold probability was more than 0.3, 0.5, and 0.1 in the validation set, the independent-test set 1 and the independent-test set 2 could gain more net benefits using RN than FM and CM, respectively. Conclusion: Radiomics features extracted from CT images of adrenal glands and periadrenal fat are related to disease prognosis in patients with COVID-19 and have great potential for predicting its severity.

A Radiomics Based Approach Using Adrenal Gland and Periadrenal Fat CT Images to Fairly Allocate COVID-19 Health Care Resources (preprint)

Background: COVID-19 disease severity is associated with endocrine system. We hypothesis radiomics features from the adrenal gland and periadrenal fat CT images can assess possibilities of disease exacerbation.Methods: A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed 3D V-Net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted.Findings: The auto-segmentation framework yielded a dice value of 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.717, 0.716, 0.736, 0.760, and 0.833, respectively in the validation set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was more than 0.3 in the validation set or between 0.4 and 0.8 in the test set, it could gain more net benefits using RN than FM and CM.Interpretation: Radiomics features extracted from adrenal gland and periadrenal fat CT images are related to disease exacerbation in patients with COVID-19.Funding Information: The study was supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT320003）; The Science and Technology Foundation of Guizhou Province (QKHPTRC[2019]5803); The Guiyang Science and Technology Project (ZKXM[2020]4); Beijing Medical and Health Foundation (YWJKJJHKYJJ-B20261CS) and Chongqing Science and Health Joint Medical Research Project (2021MSXM052).Declaration of Interests: The authors have declared that no conflict of interest exists.Ethics Approval Statement: This multicenter study was approved by the ethics committees of all participating hospitals (2020,NO.01). Because of its retrospective nature, the need to obtain informed consent in advance was waived. The study was performed according to the principles of the declaration of Helsinki.

Value of radiomics features from adrenal gland and periadrenal fat CT images predicting COVID-19 progression (preprint)

Background Value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied. Methods A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed 3D V-Net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted to predict disease progression in patients with COVID-19. Results The auto-segmentation framework yielded a dice value of 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.712, 0.692, 0.763, 0.791, and 0.806, respectively in the training set. FM and RN had better predictive efficacy than CM ( P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was more than 0.3 in the validation set or between 0.4 and 0.8 in the test set, it could gain more net benefits using RN than FM and CM. Conclusion Radiomics features extracted from the adrenal gland and periadrenal fat CT images may predict progression in patients with COVID-19. Funding This study was funded by Science and Technology Foundation of Guizhou Province (QKHZC [2020]4Y002, QKHPTRC [2019]5803), the Guiyang Science and Technology Project (ZKXM [2020]4), Guizhou Science and Technology Department Key Lab. Project (QKF [2017]25), Beijing Medical and Health Foundation (YWJKJJHKYJJ-B20261CS) and the special fund for basic Research Operating Expenses of public welfare research institutes at the central level from Chinese Academy of Medical Sciences (2019PT320003).

Clinical characteristics of 134 convalescent patients with COVID-19 in Guizhou, China (preprint)

Background: Previous studies have focused on the clinical characteristics of hospitalized patients with the novel 2019 coronavirus disease (COVID-19). Limited data are available for convalescent patients. This study aimed to evaluate the clinical characteristics of discharged COVID-19 patients. Methods: : In this retrospective study, we extracted data for 134 convalescent patients with COVID-19 in Guizhou Provincial Staff Hospital from February 15 to March 31, 2020. Cases were analyzed on the basis of demographic, clinical, and laboratory data as well as radiological features. Results: : Of 134 convalescent patients with COVID-19, 19 (14.2%) were severe cases, while 115 (85.8%) were non-severe cases. The median patient age was 33 years (IQR, 21.8 to 46.3), and the cohort included 69 men and 65 women. Compared with non-severe cases, severe patients were older and had more chronic comorbidities, especially hypertension, diabetes, and thyroid disease (P<0.05). Leukopenia was present in 32.1% of the convalescent patients and lymphocytopenia was present in 6.7%, both of which were more common in severe patients. 48 (35.8%) of discharged patients had elevated levels of alanine aminotransferase, which was more common in adults than in children (40.2% vs 13.6%, P=0.018). A normal chest CT was found in 61 (45.5%) patients during rehabilitation. Severe patients had more ground-glass opacity, bilateral patchy shadowing, and fibrosis. No significant differences were observed in the positive rate of IgG and/or IgM antibodies between severe and non-severe patients. Conclusion: Leukopenia, lymphopenia, ground-glass opacity, and fibrosis are common in discharged severe COVID-19 patients, and liver injury is common in discharged adult patients. We suggest physicians develop follow-up treatment plans based on the different clinical characteristics of convalescent patients.

Therapeutic Effects and Molecular Network Mechanism of Chinese herbs for Coronavirus Disease-19 (preprint)

Background: The coronavirus disease-19 (COVID-19) outbreak on December 2019.The present study was aimed to explore the therapeutic effects and the network pharmacology mechanism of Chinese herbs in COVID-19 patients.Methods: In this retrospective study, demographic, clinical signs, radiography, and laboratory of 78 patients were analysis from patients' medical records. Network pharmacology was applied to characterize the action mechanism of herbs decoction. Results: Of all patients were imported cases with familial aggregation. Survival analysis showed that the proportion of cough (χ2 =3.864, P=0.049) and fever (χ2 =5.549, P=0.018) in TCM group declined faster than control group. There was a significant radiographic lesions remission difference between groups (χ2 =7.666, P=0.006). After adjusted by baseline data, the changes of Lymphocytes, ALT and LDH were greater in TCM group (P=0.023, 0.005, 0.015, respectively). A total of 1852 ingredients in 13 herbs were obtained, among which, the ingredients-target network included 168 compounds and 189 targets, 38 GO terms and 63 pathways were found in enrichment analysis. Conclusion: The therapeutic effect of Chinese herbs was amelioration of cough and fever, facilitated the absorption of inflammatory infiltrates seen in the lungs, and increased the number of lymphocytes, protection of liver function via the mechanism of inhibition of coronavirus attack organs and immune cells directly. Molecular mechanisms need to be further validate in vitro and vivo.