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 analyze the influencing factors for postoperative anastomotic leak (AL) in carcinoma of the esophagus and gastroesophageal junction and construct a nomogram predictive model. Methods The patients who underwent radical esophagectomy at Jinling Hospital Affiliated to Nanjing University School of Medicine from January 2018 to June 2020 were included in this study. Relevant variables were screened using univariate and multivariate logistic regression analyses. A nomogram was then developed to predict the risk factors associated with postoperative AL. The predictive performance of the nomogram was validated using the receiver operating characteristic (ROC) curve. Results A total of 468 patients with carcinoma of the esophagus and gastroesophageal junction were included in the study, comprising 354 males and 114 females, with a mean age of (62.8±7.2) years. The tumors were predominantly located in the middle or lower esophagus, and 51 (10.90%) patients experienced postoperative AL. Univariate logistic regression analysis indicated that age, body mass index (BMI), tumor location, preoperative albumin levels, diabetes mellitus, anastomosis technique, anastomosis site, and C-reactive protein (CRP) levels were potentially associated with AL (P<0.05). Multivariate logistic regression analysis identified age, BMI, tumor location, diabetes mellitus, anastomosis technique, and CRP levels as independent risk factors for AL (P<0.05). A nomogram was developed based on the findings from the multivariate logistic regression analysis. The area under the receiver operating characteristic (ROC) curve was 0.803, indicating a strong concordance between the actual observations and the predicted outcomes. Furthermore, decision curve analysis demonstrated that the newly established nomogram holds significant value for clinical decision-making. Conclusion The predictive model for postoperative AL in patients with carcinoma of the esophagus and gastroesophageal junction demonstrates strong predictive validity and is essential for guiding clinical monitoring, early detection, and preventive strategies.

Objective To investigate the necessity of further surgery for patients with locally advanced esophageal squamous cell carcinoma following treatment with the programmed cell death-1 (PD-1) inhibitor combined with chemotherapy, and to assess its impact on survival. Methods Patients with stage ⅡA to ⅢB esophageal squamous cell carcinoma who received immunotherapy combined with chemotherapy at our hospital from January 2020 to June 2022 were selected for this study. Based on whether they underwent surgery after receiving PD-1 inhibitor combined with chemotherapy, patients were divided into a surgery group and a non-surgery group. We compared the general clinical data, side effects, clinical complete response rates, progression-free survival (PFS), and overall survival (OS) between the two groups. Results A total of 58 patients were included in the study, comprising 45 males and 13 females, with an average age of (65.5±6.9) years. There were no statistical differences in general clinical data or adverse reactions between the two groups. Univariate analysis revealed that the objective response rate and surgery were significantly associated with PFS (P<0.05). Binary logistic regression analysis showed that surgery was the only independent risk factor for PFS (P=0.003). Kaplan-Meier survival analysis showed that the PFS and OS in the surgery group were significantly higher than those in the non-surgery group (HR=0.13, 95%CI 0.036 to 0.520, P＜0.001; HR=0.17, 95%CI 0.045 to 0.680, P=0.004). Conclusion After treatment with the PD-1 inhibitor combined with chemotherapy, patients with locally advanced esophageal squamous cell carcinoma still require surgical intervention to achieve improved PFS and OS.

Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang, SinoMed and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.670-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant therapy (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

Objective To systematically review the sex differences in efficacy of immune checkpoint inhibitors (ICIs) for non-small cell lung cancer (NSCLC) patients. Methods We conducted a computer search of Medline, The Cochrane Library, and EMbase from inception to November 2022 to identify randomized controlled trials (RCTs) assessing the efficacy of ICIs in patients with NSCLC. A meta-analysis was performed using RevMan 5.4 software. Results Finally 16 RCTs with a total of 9 653 patients were included, and the modified Jadad scale score was≥4 points. Meta-analysis results showed that in female NSCLC patients receiving immune therapy, the median overall survival (OS) [HR=0.72, 95%CI (0.61, 0.85), P<0.001] was longer than that in males [HR=0.73, 95%CI (0.69, 0.78), P<0.001]. Males [HR=0.64, 95%CI (0.58, 0.71), P<0.001] had an advantage over females [HR=0.76, 95%CI (0.57, 1.03), P=0.760] in median progression-free survival (PFS). Conclusion Females receiving ICIs have an advantage over males in terms of median OS. However, males tend to derive greater benefit from ICIs in terms of median PFS.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

In thoracoscopic pulmonary nodule resection surgery, precise preoperative planning is crucial. Artificial intelligence (AI)-assisted three-dimensional (3D) reconstruction technologies have shown great potential in this area. AI-assisted 3D reconstruction technologies can provide accurate, personalized models of the pulmonary vasculature and bronchial anatomy, assisting surgeons in detailed surgical planning and thus enhancing the precision and safety of surgeries. This article reviews the application progress of AI-assisted 3D reconstruction technologies in pulmonary nodule surgery, including their applications in preoperative diagnosis, surgical planning, and intraoperative navigation, as well as the advancements in AI-assisted 3D reconstruction technologies. It analyzes the technical features of all kinds of 3D reconstruction methods, their clinical applications, and the challenges they face.

The surgical treatment of thoraco-abdominal aortic aneurysm (TAAA) requires a unique multidisciplinary approach. A thorough preoperative examination and evaluation are essential to determine the optimal timing for surgery and to optimize organ function as needed. During the perioperative period, excellent surgical skills and an appropriate strategy for extracorporeal circulation will be employed based on the extent of the aneurysm. Additionally, necessary measures will be taken to monitor and protect the functions of vital organs. Close monitoring and management in the postoperative stage, along with early detection of complications and effective treatment, are crucial for improving the prognosis of TAAA surgery. This article reviews the current research progress in the perioperative management of TAAA surgery.

Risk prediction models for postoperative pulmonary complications (PPCs) can assist healthcare professionals in assessing the likelihood of PPCs occurring after surgery, thereby supporting rapid decision-making. This study evaluated the merits, limitations, and challenges of these models, focusing on model types, construction methods, performance, and clinical applications. The findings indicate that current risk prediction models for PPCs following lung cancer surgery demonstrate a certain level of predictive effectiveness. However, there are notable deficiencies in study design, clinical implementation, and reporting transparency. Future research should prioritize large-scale, prospective, multi-center studies that utilize multiomics approaches to ensure robust data for accurate predictions, ultimately facilitating clinical translation, adoption, and promotion.