No abstract

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

Background Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC). Methods The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups. Results We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05). Conclusion A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

No abstract

Background: Lung transplantation is a treatment for end-stage lung disease. The optimal transplant strategy for patients with end-stage lung disease complicated by pulmonary hypertension (PH) is controversial. The aim of this study is to review this experience and analyze the outcomes of lung transplantation for PH. Methods: This retrospective study collected data on patients with PH undergoing lung transplantation between March 2016 and December 2019 at a single center in China. The perioperative features and shortand medium-term outcomes between single-lung transplantation (SLT) and double-lung transplantation (DLT) were compared. Kaplan-Meier methods were used to analyze overall survival across a variety of transplantation procedures, age, mean pulmonary artery pressure (mPAP), body mass index (BMI), and indications of transplantation.Results: A total of 63 patients with PH were finally included in the analysis. The mean age, mean BMI, and mPAP were 56.37 years, 19.56 kg/m 2 , and 35.4 mmHg respectively. The overall 1-, 2-, and 3-year survival was 70%, 63%, and 60%, respectively. Five (7.94%) patients died within 30 days after surgery and nine patients (14.3%) died from infection during the followed-up period. There were no significant differences in the short-and medium-term survival outcomes of SLT and DLT, but postoperative pulmonary function was better in DLT. Patients older than 60 years of age had worse survival (P=0.01). Conclusions:The short-and medium-term survival outcomes between SLT and DLT are similar in selected patients with PH. DLT provides better pulmonary function. Patients older than 60 years are associated with worse survival.