No abstract

BackgroundFractional exhaled nitric oxide (FeNO) is an easy, sensitive, reproducible, and noninvasive marker of eosinophilic airway inflammation. Accordingly, FeNO is extensively used to diagnose and manage asthma. Patients with COPD who share some of the features of asthma have a condition called asthma–COPD overlap syndrome (ACOS). The feasibility of using FeNO to differentiate ACOS patients from asthma and COPD patients remains unclear.MethodsFrom February 2013 to May 2016, patients suspected with asthma and COPD through physician’s opinion were subjected to FeNO measurement, pulmonary function test (PFT), and bronchial hyperresponsiveness or bronchodilator test. Patients were divided into asthma alone group, COPD alone group, and ACOS group according to a clinical history, PFT values, and bronchial hyperresponsiveness or bronchodilator test. Receiver operating characteristic (ROC) curves were obtained to elucidate the clinical functions of FeNO in diagnosing ACOS. The optimal operating point was also determined.ResultsA total of 689 patients were enrolled in this study: 500 had asthma, 132 had COPD, and 57 had ACOS. The FeNO value in patients with ACOS was 27 (21.5) parts per billion (ppb; median [interquartile range]), which was significantly higher than that in the COPD group (18 [11] ppb). The area under the ROC curve was estimated to be 0.783 for FeNO. Results also revealed an optimal cutoff value of >22.5 ppb FeNO for differentiating ACOS from COPD patients (sensitivity 70%, specificity 75%).ConclusionFeNO measurement is an easy, noninvasive, and sensitive method for differentiating ACOS from COPD. This technique is a new perspective for the management of COPD patients.

The measurement of FeNO is a non-invasive, reproducible, and sensitive method of differentiating CVA patients from NCVA patients. A combination of the level of FeNO (25 ppb) and the abnormal small airway function suggested higher CVA possibility, thereby resulting in a rapid diagnosis. Unnecessary treatments are avoided. This finding provides a new perspective for the management of patients with CVA.

Background: Programmed death ligand-1 (PD-L1) expression remains a crucial predictor in selecting patients for immunotherapy. The current study aimed to non-invasively predict PD-L1 expression based on chest computed tomography (CT) images in advanced lung adenocarcinomas (LUAD), thus help select optimal patients who can potentially benefit from immunotherapy.Methods: A total of 127 patients with stage III and IV LUAD were enrolled into this study. Pretreatment enhanced thin-section CT images were available for all patients and were analyzed in terms of both morphologic characteristics by radiologists and deep learning (DL), so to further determine the association between CT features and PD-L1 expression status. Univariate analysis and multivariate logical regression analysis were applied to evaluate significant variables. For DL, the 3D DenseNet model was built and validated. The study cohort were grouped by PD-L1 Tumor Proportion Scores (TPS) cutoff value of 1% (positive/negative expression) and 50% respectively.Results: Among 127 LUAD patients, 46 (36.2%) patients were PD-L1-positive and 38 (29.9%) patients expressed PD-L1-TPS ≥50%. For morphologic characteristics, univariate and multivariate analysis revealed that only lung metastasis was significantly associated with PD-L1 expression status despite of different PD-L1 TPS cutoff values, and its Area under the receiver operating characteristic curve (AUC) for predicting PD-L1 expression were less than 0.700. On the other hand, the predictive value of DL-3D DenseNet model was higher than that of the morphologic characteristics, with AUC more than 0.750. Conclusions:The traditional morphologic CT characteristics analyzed by radiologists show limited prediction efficacy for PD-L1 expression. By contrast, CT-derived deep neural network improves the prediction efficacy, it may serve as an important alternative marker for clinical PD-L1 detection.

No abstract