Objective To assess any potential associations between lung cancer and gut microbiota. Methods Mendelian randomization (MR) analysis was carried out by utilizing summary data from genome-wide association studies (GWAS) of the gut microbiota and lung cancer. The gut microbiota served as an exposure. Instrumental ariables (IVs) were identified from the GWAS of 18340 participants. The GWAS study of lung cancer from Europe served as an outcome, including 29 266 lung cancer patients and 56450 controls. We used the inverse-variance weighted (IVW) method as the primary analysis. Sensitivity analysis was used to test the reliability of MR analysis results. Results IVW results showed that Genus Parabacteroides (OR=1.258, 95%CI 1.034 to 1.531, P=0.022) and Phylum Bacteroidetes (OR=1.192, 95%CI 1.001 to 1.419, P=0.048) had a positive causal association with lung cancer, and there was a negative causal association between family Bifidobacteriaceae (OR=0.845, 95%CI 0.721 to 0.989, P=0.037) and order Bifidobacteriales (OR=0.865, 95%CI 0.721 to 0.989, P=0.037) with lung cancer. Sensitivity analysis showed no evidence of reverse causality, pleiotropy, and heterogeneity. Conclusion This study demonstrates that Genus Parabacteroides and Phylum Bacteroidetes are related to an increased risk of lung cancer, family Bifidobacteriaceae and order Bifidobacteriales can reduce the risk of lung cancer. Our thorough investigations provide evidence in favor of a potential causal relationship between a number of gut microbiota-taxa and lung cancer. To demonstrate how gut microbiota influences the development of lung cancer, further research is necessary.
Objective To develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.