Objective To investigate the expression characteristics, prognostic value, and correlation with immune infiltration of vasoactive intestinal peptide receptor 2 (VIPR2) in esophageal adenocarcinoma (ESAD). Methods Transcriptome data of 80 ESAD and 10 normal tissues were retrieved from the TCGA database. Immune-related differential genes were identified by integrating the ImmPort database. Cox regression analysis was applied to evaluate the prognostic significance of VIPR2. GSVA and ssGSEA were employed to assess the association between VIPR2 and immune-cell infiltration. Functional enrichment was performed using GO, KEGG, and GSEA. Results VIPR2 expression was significantly downregulated in ESAD tissues (P<0.05). High VIPR2 expression was associated with prolonged overall survival (HR=0.246, P=0.047), serving as an independent prognostic factor. Fifteen immune-cell types, including CD8⁺ T cells, NK cells, and dendritic cells, showed significantly higher infiltration in the VIPR2-high group, whereas Th17-cell abundance was decreased (P<0.05). Enrichment analyses revealed that VIPR2 participated in multiple tumor-immune pathways and was positively correlated with PD-1 and CTLA-4 expression (P<0.01). Conclusion VIPR2 may serve as a potential biomarker in the prognostic assessment and could modulate the tumor-immune microenvironment in ESAD.

Nowadays, lung cancer is the most common and lethal invasive tumor type in Chinese population, challenging overall health level. However, personalized early-stage treatment is currently still not widely implemented, and the choice of treatment highly depends on experience of physician. Based on deep learning and radiomics principles, deep-radiomics is important for establishing objective and promotable precision medicine plans. Among all aspects, the explainability of a model is critical for its usage in clinical practice. This paper discusses the technical aspects of explainable deep-radiomics in lung cancer, and analyzes challenges we are facing. Non-fully supervised learning methods, as a current hotspot in deep learning technology, can construct more trustworthy and practically valuable deep learning models through the co-design method of performance-interpretability. Medical artificial intelligence faces three core challenges in transitioning from the laboratory to hospitals: high-level cognitive demands, data privacy and generalization capabilities, and regulatory compliance. However, with appropriate design, non-fully supervised learning holds the greatest potential to bridge the gap between design and application, enabling broader adoption.

Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.

Thymoma is a malignant tumor originating from thymus epithelial cells, with an incidence of 1.3-2.6 per million. Due to its low incidence, lack of cells and animal models, there are relatively few studies on thymoma, and its diagnosis and treatment progress is relatively slow. The update of 5th edition of WHO Classification of Thoracic Tumors in 2021 and the NCCN( National Comprehensive Cancer Network) Clinical Practice Guidelines in Oncology: Thymoma and Thymoma Cancer in 2024 put forward many new views and suggestions on the diagnosis and treatment strategy of thymoma. This article reviews the research and treatment of thymoma based on the latest research progress in recent years, aiming to improve the clinician's understanding of thymoma, provide reference for treatment, and promote the research of thymoma.

Objective To explore the application of artificial intelligence (AI) in the standardized training of thoracic surgery residents, specifically in enhancing clinical skills and anatomical understanding through AI-assisted lung nodule identification and lung segment anatomy teaching. Methods Thoracic surgery residents undergoing standardized training at Peking Union Medical College Hospital from September 2023 to September 2024 were selected. They were randomly assigned to a trial group and a control group using a random number table. The trial group used AI-assisted three-dimensional reconstruction technology for lung nodule identification, while the control group used conventional chest CT images. After basic teaching and self-practice, the ability to identify lung nodules on the same patient CT images was evaluated, and feedback was collected through questionnaires. Results A total of 72 residents participated in the study, including 30 (41.7%) males and 42 (58.3%) females, with an average age of (24.0±3.0) years. The trial group showed significantly better overall diagnostic accuracy for lung nodules (91.9% vs. 73.3%) and lung segment identification (100.0% vs. 83.70%) compared to the control group, and the reading time was significantly shorter [ (118.5±10.5) s vs. (332.1±20.2) s, P<0.01]. Questionnaire results indicated that 94.4% of the residents had a positive attitude toward AI technology, and 91.7% believed that it improved diagnostic accuracy. Conclusion AI-assisted teaching significantly improves thoracic surgery residents’ ability to read images and clinical thinking, providing a new direction for the reform of standardized training.

Lung cancer, particularly non-small cell lung cancer (NSCLC), is a leading cause of cancer-related mortality worldwide. In recent years, metabolomics has emerged as a key systems biology approach for analyzing small-molecule metabolites in cells, tissues and organisms. It provides new strategies for early diagnosis and metabolic profiling. Additionally, metabolomics plays a crucial role in studying resistance mechanisms in lung cancer. Tumor cell metabolic reprogramming is a key driving factor in the initiation and progression of lung cancer. Metabolomics studies have revealed how lung cancer cells regulate critical pathways such as energy metabolism, lipid metabolism, and amino acid metabolism to adapt to the demands of rapid proliferation and invasive metastasis. This review summarizes the latest advances in metabolomics research in lung cancer, focusing on the characteristics of metabolic reprogramming, the identification of potential metabolic biomarkers, and the prospects of metabolomics in early diagnosis and the elucidation of resistance mechanisms in lung cancer.
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Humans ; Metabolomics/methods* ; Lung Neoplasms/pathology* ; Animals ; Biomarkers, Tumor/metabolism*

Thymoma is a malignant tumor originating from thymus epithelial cells, with an incidence of 1.3-2.6 per million. Due to its low incidence, lack of cells and animal models, there are relatively few studies on thymoma, and its diagnosis and treatment progress is relatively slow. The update of 5th edition of WHO Classification of Thoracic Tumors in 2021 and the NCCN( National Comprehensive Cancer Network) Clinical Practice Guidelines in Oncology: Thymoma and Thymoma Cancer in 2024 put forward many new views and suggestions on the diagnosis and treatment strategy of thymoma. This article reviews the research and treatment of thymoma based on the latest research progress in recent years, aiming to improve the clinician's understanding of thymoma, provide reference for treatment, and promote the research of thymoma.

With the growth of the global economy , changes in climate and ecological environments, and increased mobility of humans and animals, the transmission risk of vector-borne tropical diseases continues to rise. To address this challenge, strengthening surveillance of vector-borne tropical diseases is urgent. This consensus brought together 29 renowned experts in related professional fields from 26 institutions in China, who, through analyzing the epidemic trend and hazard situation of vector-borne tropical diseases and summarizing the working experiences of experts, have firstly reached following consensus: the burden of vector-borne tropical diseases is heavy with great threats to human health; China has achieved remarkable results in prevention and control of vector-borne tropical diseases , but still needs to strengthen the surveillance and response actively. Secondly, a unanimous consensus has been reached on the aspects of surveillance definition, objectives, contents, and methods of vector-borne tropical diseases. Thirdly, detail requirements have been agreed including: strengthening the concept of early surveillance and forecast, standarding the function, evaluation steps, and construction requirements of surveillance system for vector-borne tropical diseases. Fourthly, key tasks were put forward that need to be investigated and strengthened in the future. This expert consensus provides a standardized reference for the construction of the surveillance pathway and surveillance system for vector-borne tropical diseases in China.

Lung cancer is one of the main causes of cancer burden and death in China,with nearly 800,000 newly diagnosed lung cancer patients each year,nearly half of whom are lung adenocarcinoma(LUAD)patients.According to cur-rent clinical guidelines,surgery is the main treatment for stage Ⅰ LUAD patients,but the 5-year overall survival rate of stage ⅠLUAD patients alone is still unsatisfactory,about 73％-90％,indicating that a considerable number of patients require other means to improve survival benefits.Chemotherapy and targeted therapy have achieved great success in the treatment of locally advanced and metastatic LUAD patients,but there is still controversy over whether they can benefit stage Ⅰ LUAD postopera-tive patients.Under the circumstances,many researchers have paid attention to this issue and made beneficial explorations.This review provides a brief review of the factors that affect the acceptance of adjuvant chemotherapy and targeted therapy in stage ⅠLUAD postoperative patients,as well as the relevant clinical research on the application of adjuvant chemotherapy and targeted therapy in stage Ⅰ LUAD postoperative patients,in order to gain a broader understanding of the latest developments in this field and find new breakthroughs to promote sustained research in this field.