A computed tomography (CT)-derived radiomics approach for predicting primary co-mutations involving TP53 and epidermal growth factor receptor (EGFR) in patients with advanced lung adenocarcinomas (LUAD).

BACKGROUND: Epidermal growth factor receptor (EGFR) co-mutated with TP53 could reduce responsiveness to tyrosine kinase inhibitors (TKIs) and worsen patients' prognosis compared to TP53 wild type patients in. EGFR: mutated lung adenocarcinomas (LUAD). To identify this genetically unique subset prior to treatment through computed tomography (CT) images had not been reported yet. METHODS: Stage III and IV LUAD with known mutation status of EGFR and TP53 from The First Affiliated Hospital of Sun Yat-sen University (May 1, 2017 to June 1, 2020) were collected. Characteristics of pretreatment enhanced-CT images were analyzed. One-versus-one was used as the multiclass classification strategy to distinguish the three subtypes of co-mutations: EGFR + & TP53 +, EGFR + & TP53 -, EGFR -. The clinical model, semantic model, radiomics model and integrated model were built. Area under the receiver-operating characteristic curves (AUCs) were used to evaluate the prediction efficacy. RESULTS: A total of 199 patients were enrolled, including 83 (42%) cases of EGFR -, 55 (28%) cases of EGFR + & TP53 +, 61 (31%) cases of EGFR + & TP53 -. Among the four different models, the integrated model displayed the best performance for all the three subtypes of co-mutations: EGFR - (AUC, 0.857; accuracy, 0.817; sensitivity, 0.998; specificity, 0.663), EGFR + & TP53 + (AUC, 0.791; accuracy, 0.758; sensitivity, 0.762; specificity, 0.783), EGFR + & TP53 - (AUC, 0.761; accuracy, 0.813; sensitivity, 0.594; specificity, 0.977). The radiomics model was slightly inferior to the integrated model. The results for the clinical and the semantic models were dissatisfactory, with AUCs less than 0.700 for all the three subtypes. CONCLUSIONS: CT imaging based artificial intelligence (AI) is expected to distinguish co-mutation status involving TP53 and EGFR. The proposed integrated model may serve as an important alternative marker for preselecting patients who will be adaptable to and sensitive to TKIs.

Sex differences in group 2 innate lymphoid cell-dominant allergic airway inflammation.

There were gender differences in the prevalence and severity of allergic diseases. Group 2 innate lymphoid cells (ILC2s) were recently reported to play a critical role in allergic diseases. We investigated the sex-dependent differences in ILC2-dominant allergic airway inflammation model using T\B cell-deficient mice, and determined the gender differences of ILC2 levels in patients with asthma and allergic rhinitis. Female mice exhibited higher levels of inflammatory infiltration and large production of IL-5 and IL-13, especially for ILC2 levels compared to male mice with the induction of IL-33. However, no significant differences were found for the levels of circulating ILC2s between the genders of patients. The treatment of testosterone significantly decreased the intracellular type 2 cytokines in ILC2s and the proliferation of pure ILC2s in response to epithelial cytokines. Our study suggested the sex differences and the involvement of androgen on ILC2s in allergic diseases.

A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas.

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.

WITHDRAWN: Clinical and CT imaging features of 2019 novel coronavirus disease (COVID-19).

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Evaluation of the Characteristics of Asthma in Severe and Extremely Severe COPD.

Background: Biotherapy for asthma may be useful in patients suffering from chronic obstructive pulmonary disease (COPD) with asthma characteristics. Therefore, the evaluation and close monitoring of asthma characteristics in severe and extremely severe COPD can guide treatment decisions to improve prognosis. Methods: Stable patients suffering from COPD and having a forced expiratory volume in 1 s (FEV1%) of ≤50% (GOLD 3-4) in the First Affiliated Hospital of Sun Yat-Sen University from December 2014 to June 2018 were retrospectively enrolled in this study and evaluated in terms of their asthma characteristics (blood eosinophil counts, fractional exhaled NO [FeNO] values, and reversibility). Results: A total of 178 patients with an average age of 65.62±9.28 years were enrolled in this study. A total of 85 patients had an improvement of ≥12% in FEV1%, and 61 of these patients had an absolute increase of >200 mL. Of 122 patients, 68 had blood eosinophil counts of ≥150 cells/µl, whereas 27 showed blood eosinophil counts ≥300 cells/µl. The blood eosinophil of ≥2% was found in 66/122 (54.10%) patients, whereas ≥3% was found in 51/122 (41.80%) patients. A total of 46 of 58 patients had an increased serum IgE level of ≥30 IU/mL, and 32 patients had an IgE of ≥100 IU/mL. The FeNO value of ≥25 ACO (ppb) was found in 51/155 (32.90%) patients. Furthermore, 43 patients had asthma-COPD overlap (ACO), and the FeNO values in the ACO group was 26.13±14.91 ppb, which was significantly higher than that in the COPD alone group (20.99±9.16 ppb; P=0.016). A total of 12 patients with ACO had a negative response after bronchodilation. In the COPD alone group, 34 patients had an absolute increase of >200 mL, whereas 55 of the 95 patients had blood eosinophil counts of ≥150 cells/µl. The blood eosinophilia of ≥2% was found in 54/95 (56.84%) patients. A total of 36 of 45 patients had an increased serum IgE level of ≥30 IU/mL. The FeNO value of 34/123 (27.64%) patients was ≥25 ppb. Conclusion: The characteristics of asthma are common findings in patients with severe and extremely severe COPD. Biomarkers should be actively used to evaluate the characteristics of asthma in these patients. If the characteristics of asthma exist, then anti-IgE or anti-IL-5 therapy should be considered to reduce exacerbation.

Importance of fractional exhaled nitric oxide in the differentiation of asthma-COPD overlap syndrome, asthma, and COPD.

BACKGROUND: Fractional 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. METHODS: From 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. RESULTS: A 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%). CONCLUSION: FeNO 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.