International journal of surgery最新文献

With the rapid advancement of generative artificial intelligence (Gen AI) technology, an increasing number of studies are integrating Gen AI into healthcare. This study analyzed 1987 English publications in this field using bibliometric methods, sourced from the Web of Science Core Collection (WOSCC). The findings reveal a significant increase in publications since 2023, with 496 publications in 2023 and 1478 publications in 2024. The most contributing and influential journal was the Journal of Medical Internet Research. The total number of publications (TP) of this journal was 66, and the total number of citations (TC) was 1108. The most contributing country/region, affiliation, and author were the United States of America (TP = 841, TC = 8740), Harvard University (TP = 89, TC = 815), and Lechien, Jerome R. (TP = 18, TC = 228), respectively. The closest partnerships were observed between the USA and China, Tel Aviv University and Chaim Sheba Medical Center, and Cheungpasitporn, Wisit, and Thongprayoon, Charat, respectively. Research topics of all publications mainly focused on the application of Gen AI in clinical diagnosis, decision support, medical education, patient education, and mental health management, while also emphasizing technical and ethical challenges. Notably, several clusters highlighted the relevance of Gen AI in surgery, underscoring its potential impact in this key branch of healthcare. The findings will provide academic insights for technology developers and policymakers, as well as guidance for future research directions.

Background: In colorectal cancer (CRC), genomic microsatellite instability (MSI) indicates potential susceptibility to immune checkpoint blockade. Tislelizumab, a programmed cell death protein-1 inhibitor, has shown efficacy in MSI-high (MSI-H)/mismatch repair-deficient (dMMR) metastatic CRC.

Methods: This prospective, single-arm phase II study (BGB-A317-214) aimed to evaluate the efficacy and safety of neoadjuvant tislelizumab in stage II-III MSI-H/dMMR CRC. Eligible patients received three cycles of neoadjuvant tislelizumab (200 mg intravenously) every 3 weeks, followed by radical surgery. The primary endpoint was investigator-assessed major pathological response (MPR) rate. Secondary endpoints included investigator-assessed pathological complete response (pCR) rate, event-free survival (EFS), and safety.

Results: From January 2022 to June 2023, 33 patients were enrolled and received at least one cycle of neoadjuvant tislelizumab, and 29 received radical surgery. At primary analysis (data cutoff: 26 September 2023; median study follow-up: 6.5 [range, 0.9-20.0] months), MPR and pCR rates were 89.7% (26/29 patients) and 62.1% (18/29 patients), respectively. MPR and pCR were consistent across the subgroups, including tumor location and clinical stage. With long-term follow-up (data cutoff: 3 January 2025; median study follow-up: 21.7 [range, 0.9-35.2] months), the median EFS by investigator was not reached. The 1-year and 2-year EFS rates were both 93.9%. The safety profile of tislelizumab was well tolerated with no unexpected safety signals; one patient (3%) had a grade ≥3 treatment-related adverse event (abdominal inflammation). All other treatment-related adverse events were mild to moderate in severity.

Conclusion: This study provides promising preliminary evidence for the efficacy and safety of neoadjuvant tislelizumab in patients with locally advanced MSI-H/dMMR CRC.

Background: Morbidity and mortality rates after surgery in patients with perforated peptic ulcer (PPU) remain high. Although several scoring systems have been proposed, only a few machine learning models have been designed to predict postoperative adverse events. This study aimed to develop and deploy a predictive model for the risk of postoperative adverse events in patients with PPU.

Methods: We analyzed data from the Perforated Peptic Ulcer Analyzing Project, which is a retrospective survey of adult patients with PPU in seven participating institutions in Japan between January 2011 and December 2022. The postoperative adverse events were defined as complications with a Clavien-Dindo classification of Grade III or higher. The variables were selected based on permutation feature importance derived from a random forest algorithm using bootstrap sampling. Internal-external cross-validation was conducted to develop and evaluate the model's performance. The final model was deployed as an interactive web application.

Results: Of the 702 potentially eligible patients, 425 were treated surgically. Of these, 78 patients (18.3%) experienced postoperative adverse events. Ten variables were selected and used to develop a random forest model that displayed favorable discriminatory ability, with a pooled area under the receiver operating characteristic curve (AUROC) of 0.83. Compared to the other systems, the random forest model outperformed the PULP (AUROC, 0.79) and Boey scores (AUROC, 0.67).

Conclusion: In this study, a machine learning model was developed and deployed to predict postoperative adverse events in patients with PPU. Further external validation is required for clinical use.

Background: Current research on sodium-glucose cotransporter 2 (SGLT2) inhibitors and kidney cancer is controversial. Recent studies suggest that ertugliflozin increases the overall cancer risk, especially kidney cancer.

Objective: This study investigated the potential mechanisms between ertugliflozin and kidney cancer through multidimensional data.

Methods: We identified core targets between ertugliflozin and clear cell renal cell carcinoma (ccRCC) through network toxicology. Feature genes were obtained through analysis of the TCGA database, and additional analysis was conducted on SLC5A2. Finally, a two-sample Mendelian randomization (MR) approach (mainly inverse variance weighted) was used to assess the association between feature genes, SGLT2 inhibition, and ccRCC or renal cell carcinoma (RCC).

Results: Through network toxicology, 15 core targets between ertugliflozin and ccRCC were identified. Cox regression analysis, ROC curve plotting, and survival analysis were conducted, and then two feature genes, SRC and ESR2, were obtained. Additionally, the expression of SLC5A2 was lower in tumor tissues compared to normal tissues. MR analysis results showed that SRC was not associated with RCC/ccRCC risk, while ESR2 may be associated with a higher risk of ccRCC (P = 0.03). SGLT2 inhibition potentially led to a higher risk of renal cell carcinoma [odds ratio (OR) 3.05; 95% confidence interval 1.03-9.04; P = 0.04]. When using UK Biobank Genome-Wide Association Study data, SGLT2 inhibition was associated with a higher risk of ccRCC (OR 83.70; 95%CI 3.60-1946.03; P < 0.01). Experiments verified that ertugliflozin acted on ESR2 rather than SRC.

Conclusion: Ertugliflozin may affect kidney cancer by targeting ESR2. SGLT2 inhibition may also be a contributing factor to renal cancer, and further investigation is required.