Objective Previous studies have reported a correlation between coronavirus disease-2019 (COVID-19) and asthma. However, data on whether asthma constitutes a risk factor for COVID-19 and the prevalence of asthma in COVID-19 cases still remains scant. Here, we interrogated and analyzed the association between COVID-19 and asthma. Methods In this study, we systematically searched PubMed, Embase, and Web of Science databases for studies published between January 1, to August 28, 2020. We included studies that reported the epidemiological and clinical features of COVID-19 and its prevalence in asthma patients. We excluded reviews, animal trails, single case reports, small case series and studies evaluating other coronavirus-related illnesses. Raw data from the studies were pooled into a meta-analysis. Results We analyzed findings from 18 studies, including asthma patients with COVID-19. The pooled prevalence of asthma in COVID-19 cases was 0.08 (95% CI, 0.06-0.11), with an overall I 2 of 99.07%, p < 0.005 . The data indicated that asthma did not increase the risk of developing severe COVID-19 (odds ratio [OR] 1.04 (95% CI, 0.75-1.46) p = 0.28; I 2 =20%). In addition, there was no significant difference in the incidence of asthma with analyze age in COVID-19 infections [OR] 0.77(95% CI, 0.59–1.00) p = 0.24; I 2 =29%). Conclusion Taken together, our data suggested that asthma is not a significant risk factor for the development of severe COVID-19.

Neo-chemoradiotherapy (nCRT) before surgery is a standard treatment for locally advanced esophageal cancers. However, the treatment outcome of nCRT varied with different patients. This study aimed to identify potential biomarkers for prediction of nCRT-response in patients with esophageal squamous cell carcinoma (ESCC). Microarray datasets of nCRT responder and non-responder samples (access number GSE45670 and GSE59974) of patients with ESCC were downloaded from Gene Expression Omnibus (GEO) database. The mRNA expression profiles of cancer biopsies from four ESCC patients were analyzed before and after nCRT. Differentially expressed genes (DEGs) and miRNAs were screened between nCRT responder and non-responder ESCC samples. Functional enrichment analysis was conducted for these DEGs followed by construction of protein-protein interaction (PPI) network and miRNA-mRNA regulatory network. Finally, univariate survival analysis was performed to identify candidate biomarkers with prognostic values in ESCC. We identified numerous DEGs and differentially expressed miRNAs from nCRT responder group. GO and KEGG analysis showed that the dysregulated genes were mainly involved in biological processes and pathways, including “response to stimulus”, “cellular response to organic substance”, “regulation of signal transduction”, “AGE-RAGE signaling pathway in diabetic complications”, and “steroid hormone biosynthesis”. After integration of PPI network and miRNA-mRNA network analysis, we found eight genes, TNF, AKR1C1, AKR1C2, ICAM1, GPR68, GNB4, SERPINE1 and MMP12, could be candidate genes associated with disease progression. Univariate cox regression analysis showed that there was no significant correlation between dysregulated miRNAs (such as hsa-miR-34b-3p, hsa-miR-127-5p, hsa-miR-144-3p, and hsa-miR-486-5p, et al.) and overall survival of ESCC patients. Moreover, abnormal expression of MMP12 was significantly correlated with pathological degree, TNM stage, lymph nodes metastasis, and overall survival of ESCC patients (p &lt; 0.05). Taken together, our study identified that MMP12 might be a useful tumor biomarker and therapeutic target for ESCC.

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ObjectivesTo develop and validate a Computed Tomography (CT) based radiomics nomogram for preoperative predicting of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC) patientsMethodsA total of 153 patients were randomly assigned to training and internal test sets (7:3). 46 patients were recruited to serve as an external test set. A radiologist with 8 years of experience segmented the images. Radiomics features were extracted from each image and Delta-radiomics features were calculated. Features were selected by using one way analysis of variance and the least absolute shrinkage and selection operator in the training set. K-nearest neighbor, logistic regression, decision tree, linear-support vector machine (linear -SVM), gaussian-SVM, and polynomial-SVM were used to build 6 radiomics models. Next, a radiomics signature score (Rad-score) was constructed by using the linear combination of selected features weighted by their corresponding coefficients. Finally, a nomogram was constructed combining the clinical risk factors with Rad-scores. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were performed on the three sets to evaluate the nomogram’s performance.Results4 radiomics features were selected. The six models showed the certain value of radiomics, with area under the curves (AUCs) from 0.642 to 0.701. The nomogram combining the Rad-score and clinical risk factors (radiologists’ interpretation) showed good performance (internal test set: AUC 0.750; external test set: AUC 0.797). Calibration curve and DCA demonstrated good performance of the nomogram.ConclusionOur radiomics nomogram incorporating the radiomics and radiologists’ interpretation has utility in the identification of ETE in PTC patients.

Background Eosinophilic chronic rhinosinusitis with nasal polyps (Eos-CRSwNP) remains a recalcitrant disease with a high recurrence rate. Objective This study aimed to identify a predictor of long-term recurrence in patients with Eos-CRSwNP. Methods A total of 39 Eos-CRSwNP patients who had their initial and recurrent nasal polyps surgically removed were retrospectively included in this study, with 49 Eos-CRSwNP patients without recurrence and 32 patients with non-Eos-CRSwNP matched by randomly chosen. Clinical characteristics were compared among or between groups. Spearman correlation analyses and a backward stepwise multivariate logistic regression analysis were performed to find factors associated with the recurrence and recurrence time of Eos-CRSwNP. Furthermore, a receiver operating characteristic (ROC) curve was used to determine the predictor of long-term Eos-CRSwNP recurrence. Results The number and ratio of tissue eosinophils were highest in Eos-CRSwNP with recurrence and lowest in non-Eos-CRSwNP. The ratio of tissue lymphocytes was highest in non-Eos-CRSwNP and lowest in Eos-CRSwNP with recurrence, with the number of tissue lymphocytes higher in Eos-CRSwNP without recurrence than the other two groups. The numbers of tissue lymphocytes in the initial nasal polyps were lower and the numbers of tissue eosinophils were higher in the group of recurrent nasal polyps that recurred at >5 years after surgery than in the nasal polyps that recurred at <5 years after surgery. The tissue lymphocyte-to-eosinophil ratio (LER) showed a significant negative correlation with the recurrence and the recurrence time of Eos-CRSwNP. A ROC curve revealed that a tissue LER value < 0.67 predicted long-term Eos-CRSwNP recurrence with 72.73% sensitivity and 82.35% specificity (area under the curve  =  0.789). Conclusion Tissue LER is strongly associated with Eos-CRSwNP recurrence and may play a key role in predicting long-term Eos-CRSwNP recurrence.

ObjectiveTo investigate the application of computed tomography (CT)-based radiomics model for prediction of thyroid capsule invasion (TCI) in papillary thyroid carcinoma (PTC).MethodsThis retrospective study recruited 412 consecutive PTC patients from two independent institutions and randomly assigned to training (n=265), internal test (n=114) and external test (n=33) cohorts. Radiomics features were extracted from non-contrast (NC) and artery phase (AP) CT scans. We also calculated delta radiomics features, which are defined as the absolute differences between the extracted radiomics features. One-way analysis of variance and least absolute shrinkage and selection operator were used to select optimal radiomics features. Then, six supervised machine learning radiomics models (k-nearest neighbor, logistic regression, decision tree, linear support vector machine [L-SVM], Gaussian-SVM, and polynomial-SVM) were constructed. Univariate was used to select clinicoradiological risk factors. Combined models including optimal radiomics features and clinicoradiological risk factors were constructed by these six classifiers. The prediction performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsIn the internal test cohort, the best combined model (L-SVM, AUC=0.820 [95% CI 0.758–0.888]) performed better than the best radiomics model (L-SVM, AUC = 0.733 [95% CI 0.654–0.812]) and the clinical model (AUC = 0.709 [95% CI 0.649–0.783]). Combined-L-SVM model combines 23 radiomics features and 1 clinicoradiological risk factor (CT-reported TCI). In the external test cohort, the AUC was 0.776 (0.625–0.904) in the combined-L-SVM model, showing that the model is stable. DCA demonstrated that the combined model was clinically useful.ConclusionsOur combined model based on machine learning incorporated with CT radiomics features and the clinicoradiological risk factor shows good predictive ability for TCI in PTC.

PURPOSE: To identify the value of a computed tomography (CT)-based radiomics model to predict probability of early recurrence (ER) in patients diagnosed with laryngeal squamous cell carcinoma (LSCC) after surgery. MATERIALS AND METHOD: Pre-operative CT scans of 140 LSCC patients treated by surgery are reviewed and selected. These patients are randomly split into the training set (n = 97) and test set (n = 43). The regions of interest of each patient were delineated manually by two senior radiologists. Radiomics features are extracted from CT images acquired in non-enhanced, arterial, and venous phases. Variance threshold, one-way ANOVA, and least absolute shrinkage and selection operator algorithm are used for feature selection. Then, radiomics models are built with five algorithms namely, k-nearest neighbor (KNN), logistic regression (LR), linear support vector machine (LSVM), radial basis function SVM (RSVM), and polynomial SVM (PSVM). Clinical factors are selected using univariate and multivariate logistic regressions. Last, a radiomics nomogram incorporating the radiomics signature and clinical factors is built to predict ER and its efficiency is evaluated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) is also used to evaluate clinical usefulness. RESULTS: Four features are remarkably associated with ER in patients with LSCC. Applying to test set, the area under the ROC curves (AUCs) of KNN, LR, LSVM, RSVM, and PSVM are 0.936, 0.855, 0.845, 0.829, and 0.794, respectively. The radiomics nomogram shows better discrimination (with AUC: 0.939, 95% CI: 0.867–0.989) than the best radiomics model and the clinical model. Predicted and actual ERs in the calibration curves are in good agreement. DCA shows that the radiomics nomogram is clinically useful. CONCLUSION: The radiomics nomogram, as a noninvasive prediction tool, exhibits favorable performance for ER prediction of LSCC patients after surgery.

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