Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.
Acute pulmonary embolism (PE) is a common disorder with significant morbidity and mortality in patients who underwent pulmonary ground-glass nodules (GGN) resection. We should make efforts to increase surgeons' awareness of risk factors of PE and their understanding of the effectiveness of prevention strategies. Using the optimal risk assessment model to identify high-risk patients and give them the individualized prophylaxis. Early diagnosis and accurate risk stratification is mandatory to reduce the rates of PE, to decrease health care costs and shorten the length of stay. This article summarizes the risk factors, diagnostic process, risk assessment models, prophylaxis and therapy for the PE patients who underwent GGN resection.
Objective To systematically review the research progress of artificial intelligence (AI) combined with multimodal data in the field of malignant risk assessment for thyroid nodules (TN), analyze current technological application bottlenecks and future development directions. MethodsRelevant literatures on the application of AI combined with multimodal data in TN risk assessment in recent years were retrieved and reviewed. ResultsAI, by integrating multimodal data such as ultrasound, cytopathology, and molecular markers, can effectively address the limitations of subjectivity in traditional ultrasound evaluation, uncertainty results of fine needle aspiration biopsy and diagnostic blind spots associated with single molecular markers. However, current research still faces challenges including insufficient generalization ability of small sample, lack of clinical interpretability in black-box algorithms, and insufficient standardization of multimodal data. To tackle these challenges, strategies such as promoting federated learning for multi-center data sharing, establishing interpretable AI integrated with clinical diagnostic pathways, and optimizing deep integration of liquid biopsy with AI provide new directions for overcoming existing obstacles. ConclusionAI combined with multimodal data offers an innovative technical pathway for TN malignancy risk assessment, which is expected to address inherent limitations of traditional diagnostic models and facilitate comprehensive precision management from risk stratification to prognostic monitoring.