With the advancement and development of computer technology, the medical decision-making system based on artificial intelligence (AI) has been widely applied in clinical practice. In the perioperative period of cardiovascular surgery, AI can be applied to preoperative diagnosis, intraoperative, and postoperative risk management. This article introduces the application and development of AI during the perioperative period of cardiovascular surgery, including preoperative auxiliary diagnosis, intraoperative risk management, postoperative management, and full process auxiliary decision-making management. At the same time, it explores the challenges and limitations of the application of AI and looks forward to the future development direction.

Objective To establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. Methods Patients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. Results A total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. Conclusion For patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.

Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

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 discuss the main pathological types and imaging characteristics of pulmonary nodules that are highly suspected to be malignant in clinical practice but are pathologically confirmed to be benign. Methods A retrospective analysis was performed on the clinical data of patients with pulmonary nodules who were initially highly suspected of malignancy but were subsequently pathologically confirmed to be benign. These patients were treated at the First Affiliated Hospital of Xiamen University from December 2020 to April 2023. Based on the outcomes of preoperative discussions, the patients were categorized into a benign group and a suspicious malignancy group. The clinical data and imaging characteristics of both groups were compared. Results A total of 232 patients were included in the study, comprising 112 males and 120 females, with a mean age of (50.7±12.0) years. Among these, 127 patients were classified into the benign group, while 105 patients were categorized into the suspicious malignancy group. No statistically significant differences were observed between the two groups regarding age, gender, symptoms, smoking history, or tumor history (P>0.05). However, significant differences were noted in nodule density, CT values, margins, shapes, and malignant signs (P＜0.05). Further analysis revealed that in the suspicious malignancy group, solid nodules were predominantly characterized by collagen nodules and fibrous tissue hyperplasia (33.3%), followed by tuberculosis (20.4%) and fungal infections (18.5%). In contrast, non-solid nodules were primarily composed of collagen nodules and fibrous tissue hyperplasia (41.2%) and atypical adenomatous hyperplasia (17.7%). Conclusion Benign pulmonary nodules that are suspected to be malignant are pathologically characterized by the presence of collagen nodules, fibrous tissue hyperplasia, tuberculosis, atypical adenomatous hyperplasia, and fungal infections. Radiologically, these nodules typically present as non-solid lesions and may exhibit features suggestive of malignancy, including spiculation, lobulation, cavitation, and pleural retraction.

Objective To establish an individualized nomogram model and evaluate its efficacy to provide a possible evaluation basis for the prognosis of lower third and abdominal part of oesophageal adenocarcinoma (EAC). Methods Lower third and abdominal part of EAC patients from 2010 to 2015 were chosen from the SEER Research Plus Database (17 Regs, 2022nov sub). The patients were randomly allocated to the training cohort and the internal validation cohort with a ratio of 7∶3 using bootstrap resampling. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) in EAC patients, which would be elected to construct the nomogram prediction model. C-index, calibration curve and receiver operating characteristic (ROC) curve were performed to evaluate its efficacy. Finally, the efficacy to evaluate the OS of EAC patients was compared between the nomogram prediction model and TNM staging system. Results In total, 3945 patients with lower third and abdominal part of EAC were enrolled, including 3475 males and 470 females with a median age of 65 (57-72) years. The 2761 patients were allocated to the training cohort and the remaining 1184 patients to the internal validation cohort. In the training and the internal validation cohorts, the C-index of the nomogram model was 0.705 and 0.713, respectively. Meanwhile, the calibration curve also suggested that the nomogram model had a strong capability of predicting 1-, 3-, and 5-year OS rates of EAC patients. The nomogram also had a higher efficacy than the TNM staging system in predicting 1-, 3-, and 5-year OS rates of EAC patients. Conclusion This nomogram prediction model has a high efficiency for predicting OS in the patients with lower third and abdominal part of EAC, which is higher than that of the current TNM staging system.

Objective To systematically evaluate the risk prediction model of anastomotic fistula after radical resection of esophageal cancer, and to provide objective basis for selecting a suitable model. Methods A comprehensive search was conducted on Chinese and English databases including CNKI, Wanfang, VIP, CBM, PubMed, EMbase, Web of Science, The Cochrane Library for relevant studies on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer from inception to April 30, 2023. Two researchers independently screened literatures and extracted data information. PROBAST tool was used to assess the risk of bias and applicability of included literatures. Meta-analysis was performed on the predictive value of common predictors in the model with RevMan 5.3 software. Results A total of 18 studies were included, including 11 Chinese literatures and 7 English literatures. The area under the curve (AUC) of the prediction models ranged from 0.68 to 0.954, and the AUC of 10 models was >0.8, indicating that the prediction performance was good, but the risk of bias in the included studies was high, mainly in the field of research design and data analysis. The results of the meta-analysis on common predictors showed that age, history of hypertension, history of diabetes, C-reactive protein, history of preoperative chemotherapy, hypoproteinemia, peripheral vascular disease, pulmonary infection, and calcification of gastric omental vascular branches are effective predictors for the occurrence of anastomotic leakage after radical surgery for esophageal cancer (P<0.05). Conclusion The study on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer is still in the development stage. Future studies can refer to the common predictors summarized by this study, and select appropriate methods to develop and verify the anastomotic fistula prediction model in combination with clinical practice, so as to provide targeted preventive measures for patients with high-risk anastomotic fistula as soon as possible.

Liver fibrosis is a chronic liver injury resulting from factors like viral hepatitis, autoimmune hepatitis, non-alcoholic steatohepatitis, fatty liver disease, and cholestatic liver disease. Liver transplantation is currently the gold standard for treating severe liver diseases. However, it is limited by a shortage of donor organs and the necessity for lifelong immunosuppressive therapy. Mesenchymal stem cells (MSCs) can differentiate into various liver cells and enhance liver function when transplanted into patients due to their differentiation and proliferation capabilities. Therefore, it can be used as an alternative therapy for treating liver diseases, especially for liver cirrhosis, liver failure, and liver transplant complications. However, due to the potential tumorigenic effects of MSCs, researchers are exploring a new approach to treating liver fibrosis using extracellular vesicles (exosomes) secreted by stem cells. Many studies show that exosomes released by stem cells can promote liver injury repair through various pathways, contributing to the treatment of liver fibrosis. In this review, we focus on the molecular mechanisms by which stem cell exosomes affect liver fibrosis through different pathways and their potential therapeutic targets. Additionally, we discuss the advantages of exosome therapy over stem cell therapy and the possible future directions of exosome research, including the prospects for clinical applications and the challenges to be overcome.

Liver fibrosis is a chronic liver injury resulting from factors like viral hepatitis, autoimmune hepatitis, non-alcoholic steatohepatitis, fatty liver disease, and cholestatic liver disease. Liver transplantation is currently the gold standard for treating severe liver diseases. However, it is limited by a shortage of donor organs and the necessity for lifelong immunosuppressive therapy. Mesenchymal stem cells (MSCs) can differentiate into various liver cells and enhance liver function when transplanted into patients due to their differentiation and proliferation capabilities. Therefore, it can be used as an alternative therapy for treating liver diseases, especially for liver cirrhosis, liver failure, and liver transplant complications. However, due to the potential tumorigenic effects of MSCs, researchers are exploring a new approach to treating liver fibrosis using extracellular vesicles (exosomes) secreted by stem cells. Many studies show that exosomes released by stem cells can promote liver injury repair through various pathways, contributing to the treatment of liver fibrosis. In this review, we focus on the molecular mechanisms by which stem cell exosomes affect liver fibrosis through different pathways and their potential therapeutic targets. Additionally, we discuss the advantages of exosome therapy over stem cell therapy and the possible future directions of exosome research, including the prospects for clinical applications and the challenges to be overcome.