Objective To explore the impact of number of positive regional lymph nodes (nPRLN) in N1 stage on the prognosis of non-small cell lung cancer (NSCLC) patients. Methods Patients with TxN1M0 stage NSCLC who underwent lobectomy and mediastinal lymph node dissection from 2010 to 2015 were screened from SEER database (17 Regs, 2022nov sub). The optimal cutoff value of nPRLN was determined using X-tile software, and patients were divided into 2 groups according to the cutoff value: a nPRLN≤optimal cutoff group and a nPRLN>optimal cutoff group. The influence of confounding factors was minimized by propensity score matching (PSM) at a ratio of 1 : 1. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate overall survival (OS) and lung cancer-specific survival (LCSS) of patients. Results A total of 1316 patients with TxN1M0 stage NSCLC were included, including 662 males and 654 females, with a median age of 67 (60, 73) years. The optimal cutoff value of nPRLN was 3, with 1165 patients in the nPRLN≤3 group and 151 patients in the nPRLN>3 group. After PSM, there were 138 patients in each group. Regardless of before or after PSM, OS and LCSS of patients in the nPRLN≤3 group were superior to those in the nPRLN>3 group (P<0.001). N1 stage nPRLN>3 was an independent prognostic risk factor for OS [HR=1.52, 95%CI (1.22, 1.89), P<0.001] and LCSS [HR=1.72, 95%CI (1.36, 2.18), P<0.001]. Conclusion N1 stage nPRLN>3 is an independent prognostic risk factor for NSCLC patients in TxN1M0 stage, which may provide new evidence for future revision of TNM staging N1 stage subclassification.

Objective To compare the cost and efficacy of ambulatory versus hospitalized surgery for lung tumor patients. Methods Two researchers independently conducted a computer search on February 14, 2025, in databases including CNKI, PubMed, Web of Science, Ovid Medline, Cochrane Library, and Wanfang Database, with the search period covering from the inception of these databases to February 2025. The outcome indicators were postoperative complication rate, length of hospital stay, hospitalization costs, et al. For the included randomized controlled trials and non-randomized controlled trials, we used the Cochrane risk of bias assessment tool and the Newcastle Ottawa Scale (NOS) respectively to evaluate the quality of the literature, and extracted data from the included studies for meta-analysis using RevMan 5.4 and Stata 18.0. Results A total of 12 researches were ultimately included, consisting of 2 randomized controlled trials, 2 prospective cohort studies, and 8 retrospective cohort studies, involving a total of 76 403 patients. All researches were evaluated as high-quality. Meta-analysis showed that the ambulatory surgery group had advantages over the hospitalized surgery group in terms of operation time [MD=−21.07, 95%CI (−30.55, −11.58), P<0.001], length of hospital stay [MD=−2.17, 95%CI (−3.25, −1.09), P<0.001], hospitalization costs [SMD=−1.22, 95%CI (−2.18, −0.26), P=0.01], and overall postoperative complications [OR=0.48, 95%CI (0.32, 0.74), P<0.001]. There was no statistically significant difference between the two groups in terms of postoperative hoarseness [OR=0.62, 95%CI (0.24, 1.61), P=0.33] and postoperative chylothorax [OR=0.27, 95%CI (0.07, 1.07), P=0.06]. Conclusion Compared to conventional hospitalized lung tumor resection, ambulatory lung tumor resection can significantly reduce the patient’s surgery and hospital stay time, decrease hospitalization costs, and reduce the incidence of postoperative complications. Ambulatory surgery can improve hospital efficiency while reducing the economic burden on patients. It is worthy of further promotion and application.

Objective To propose a lightweight end-to-end neural network model for automated Korotkoff sound phase recognition and subsequent blood pressure (BP) measurement, aiming to improve measurement accuracy and population adaptability. Methods We developed a streamlined architecture integrating depthwise separable convolution (DSConv), multi-head attention (MHA), and bidirectional gated recurrent unit (BiGRU). The model directly processes Korotkoff sound time-series signals to identify auscultatory phases. Systolic BP (SBP) and diastolic BP (DBP) were determined using phase Ⅰ and phaseⅤdetections, respectively. Given the clinical relevance of phase Ⅳ for specific populations (e.g., children and pregnant women, denoted as DBPⅣ), BP values from this phase were also recorded.Results The study enrolled 106 volunteers with 70 males and 36 females at mean age of (40.0±12.0) years. The model achieved 94.25% phase recognition accuracy. Measurement errors were (0.1±2.5) mm Hg (SBP), (0.9±3.4) mm Hg (DBPⅣ), and (0.8±2.6) mm Hg (DBP). Conclusion Our method enables precise phase recognition and BP measurement, demonstrating potential for developing population-adaptive blood pressure monitoring systems.

Objective To explore the correlation between lymph node metastasis and clinicopathological features of lung adenocarcinoma with diameter≤3 cm. Methods The clinicopathologic data of the patients with lung adenocarcinoma≤3 cm in diameter were retrospectively analyzed. The relationship between lymph node metastasis and age, gender, smoking history, pathological subtype, tumor diameter, pleural invasion, vascular invasion and other factors was analyzed. The risk factors of lymph node metastasis were analyzed by univariate and multivariate logistic regression. Results Finally 1 718 patients were collected, including 697 males and 1 021 females with an average age of (58.89±9.85) years. The total lymph node metastasis rate was 12.9%, among whom 452 patients of adenocarcinoma in situ and minimally invasive adenocarcinoma did not have lymph node metastasis, and the lymph node metastasis rate of invasive lung adenocarcinoma was 17.5%. Multivariate analysis showed that tumor diameter, micropapillary subtype, solid subtype, micropapillary component, solid component, vascular invasion and pleural invasion were independent risk factors for lymph node metastasis of invasive lung adenocarcinoma with diameter≤3 cm (P<0.05). While age, lepidic subtype and lepidic component were independent protective factors for lymph node metastasis (P<0.05). Conclusion Clinicopathological features can help predict lymph node metastasis of lung adenocarcinoma with diameter≤3 cm.

Objective To longitudinally investigate the characteristics of postoperative weight changes in patients with esophageal cancer and analyze its influencing factors, which can provide certain guidance for nutritional intervention in patients with esophageal cancer. Methods Patients with esophageal cancer who underwent surgical treatment at the Sichuan Cancer Hospital from December 2020 to February 2022 were prospectively included. The general information questionnaire and body composition analyzer were used to longitudinally investigate the patients’ weight and body composition before surgery (T0), 1 month after surgery (T1), 3 months after surgery (T2) and 6 months after surgery (T3), and the change characteristics were analyzed. The generalized estimating equation was used to analyze the influencing factors for postoperative weight changes in patients with esophageal cancer. Results A total of 130 patients were enrolled, including 110 males and 20 females, aged 42-79 (63.33±8.16) years. The weight and body composition of patients with esophageal cancer showed a continuous slow downward trend within 6 months after surgery. The weight loss rate of patients at 1, 3, and 6 months after surgery was 5.10%, 7.76%, and 9.86%, respectively. The analysis results of the influencing factors for postoperative weight showed that patients with the following characteristics had more weight loss: female (β=−7.703, P=0.001), ≥60 years (β=−3.657, P=0.010), smoking (β=4.622, P=0.010), low tumor differentiation degree (β=4.314, P=0.039), and high frequency of eating (β=−3.400, P=0.008). Conclusion Weight loss is an important health problem for patients with esophageal cancer after surgery, and patients have a continuous downward trend in weight within 6 months after surgery. Medical staff should pay special attention to the patients who are female, ≥60 years, having smoking history and low tumor differentiation degree.

Objective To evaluate the efficacy and safety of intraoperative pleural irrigation with Nocardia rubra cell-wall skeleton (N-CWS) for reducing pleural effusion drainage after radical surgery for lung cancer. Methods A retrospective analysis was conducted on the clinical data of lung cancer patients who underwent lobectomy and mediastinal lymph node dissection at the First Affiliated Hospital of Xiamen University between December 2024 and May 2025. Patients were divided into a control group and an irrigation group based on the intraoperative use of N-CWS. Patients in the irrigation group received pleural irrigation with 800 μg of N-CWS diluted in 10 mL of normal saline. The following outcomes were compared between the two groups: pleural effusion drainage volume at 0-24 h, 24-48 h, and 48-72 h postoperatively, degree of air leak, chest tube duration, postoperative length of stay, and the incidence of adverse events (fever, chest pain, and nausea and vomiting). Results A total of 245 patients were included (97 males, 148 females) with a mean age of (61.28±6.26) years, with 205 in the control group and 40 in the irrigation group. Compared to the control group, the irrigation group showed significantly lower pleural effusion drainage volumes at 0-24 h, 24-48 h, and 48-72 h, as well as shorter chest tube duration and postoperative length of stay (all P<0.05). There was no statistical difference in the degree of postoperative air leak (P=0.661). No significant differences were observed between the two groups regarding the highest body temperature within 72 h post-surgery (P=0.130), fever grade (P=0.196), severity of chest pain (P=0.105), or the incidence of nausea and vomiting (P=0.376). Conclusion Intraoperative pleural irrigation with N-CWS in patients undergoing lobectomy and mediastinal lymph node dissection for lung cancer can significantly reduce postoperative pleural effusion drainage volume, shorten chest tube duration and length of hospital stay. The procedure is safe and feasible.

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.