International journal of surgery最新文献

Background: In recent decades, thermal ablation (TA) has gained acceptance as an effective and safe treatment for benign thyroid nodules (BTNs). However, despite its increasing popularity, the indications and techniques of TA for BTNs lack a unified standard, resulting in differences in treatment outcomes. In particular, the current guidelines and consensus statements adopt indications based on surgical criteria, which focus on larger BTNs with symptoms or cosmetic concerns. However, these indications may not adequately demonstrate the advantages of TA, as it is a fundamentally distinct therapeutic approach. To establish novel and specific indications for TA in BTNs and to standardize the use of this technique, a panel of experts issued the current expert consensus.

Materials and methods: Based on a systematic review of the literature and clinical experience, the drafting group developed preliminary recommendations on TA for BTNs. A multidisciplinary panel of 30 experts with specific competence and expertise in TA for thyroid nodules reviewed, rated, and revised these recommendations through multiple rounds of the modified Delphi method.

Results: Twenty-six recommendations on TA for BTNs were proposed in the present consensus, covering indications and contraindications, physician training suggestions, preablation preparation, technical procedures, complications, efficacy assessment, follow-up strategies, and postablation management.

Conclusion: The present consensus emphasizes the indication of TA for BTNs and outlined the technique details and periablation management. The implementation of this consensus is expected to standardize treatment practices, enhance patient outcomes, and shape future research and policy developments in the management of BTNs.

Background: The rising incidence of thyroid nodules has led to increased referrals under the 2-week wait pathway, despite a low malignancy rate. This places pressure on healthcare systems. We developed a multidisciplinary one-stop thyroid clinic (OSTC) to streamline diagnostics and improve patient experience.

Methods: The OSTC was established in three phases: incorporating real-time ultrasound-guided fine needle aspiration (FNA), rapid on-site evaluation (ROSE) of cytology, and same-day multidisciplinary team (MDT) decision-making. Patients referred with thyroid nodules underwent clinical, biochemical, radiological, and cytological assessment, with results and management plans delivered on the same day. Data on demographics, investigations, cytology, surgery, histology, and follow-up were collected prospectively from January 2022 to October 2023. The RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) was used to evaluate implementation.

Results: A total of 508 patients were assessed; 81% were female with a median age of 51 years. FNA was performed in 46% of cases. ROSE reduced non-diagnostic (Thy1) cytology from 16% to 5%. About 62% of patients received a definitive management plan on the same day; 29% were referred for surgery, and 10% were diagnosed with thyroid cancer. The non-attendance rate in OSTC was significantly lower (1. 6%) than standard clinics (12. 4%; P < 0. 0001). Same-day MDT review facilitated prompt decisions and reduced diagnostic delays. RE-AIM evaluation confirmed wide reach, improved diagnostic yield, team-wide adoption, efficiency, and sustainability.

Conclusion: The OSTC model delivers faster, more accurate thyroid nodule assessment with reduced appointment burden and improved diagnostic yield. It is a reproducible and cost-effective model that supports NHS priorities and enhances patient-centered care.

Background: China continues to face a substantial burden of gastric cancer (GC), particularly with respect to metastasis-related mortality. However, population-based analyses of distant metastasis patterns in Chinese GC patients remain unavailable.

Methods: Global Burden of Disease (GBD) data on GC from the 1990 to 2021 period was obtained through the Global Health Data Exchange (GHDx) query tool and integrative data of 18 919 patients who underwent surgery were obtained from our hospital. Univariate and multivariate logistic regression identified independent risk factors for metastases, and survival analysis utilized univariate and multivariate Cox regression, Kaplan-Meier method, and log-rank test. Predictive nomograms were assessed using metrics such as the area under the curve (AUC), calibration curves, and decision curve analysis.

Results: According to the GBD database, GC demonstrates declining global trends in both incidence and mortality. Nevertheless, China continues to face a substantial GC burden, with progressive annual rises in distant metastasis prevalence and metastasis-related mortality. Clinical characteristics and temporal patterns vary significantly across metastatic types. Furthermore, metastatic profiles exhibit sex-, age-, and stage-specific variations. Univariate and multivariate regression analyses identified independent risk factors for overall GC metastasis and site-specific metastases. The resulting prediction models demonstrated excellent predictive accuracy for metastatic progression. The prognostic nomogram was developed to predict 1-, 5-, and 10-year overall survival (OS) in GC patients, with AUCs of 0.86 (0.84-0.88), 0.87 (0.85-0.89), and 0.80 (0.74-0.85) in the training set, respectively, which showed good discriminative ability.

Conclusions: In this study, metastatic spectrums across diverse patient subgroups and temporal patterns of metastasis in GC were investigated. Furthermore, we developed clinical predictive nomograms for various metastatic patterns and OS in GC, which enhance the understanding of metastatic behavior and provide a robust tool for personalized risk assessment and prognosis prediction.

Objectives: To evaluate the application value of three ChatGPT versions and Gemini in pathology report simplification tasks for prostate cancer.

Methods: This retrospective study assessed GPT-3.5, GPT-4.0, GPT-4o, and Gemini on pathology reports from 228 prostate cancer patients across two institutions. Data were split into internal (center 1, n = 171) and external (center 2, n = 57) cohorts. Using specific prompts, models generated simplified texts. The evaluation of outputs included three main dimensions: (1) human scoring by patients, clinicians, and pathologists; (2) readability scores; and (3) BERT-based semantic similarity scores. Statistical comparisons employed paired t -tests or Wilcoxon signed-rank tests. Statistical consistency between raters was assessed using squared weighted kappa, intraclass correlation coefficient(3,1), and percent agreement, with 95% confidence intervals calculated for all metrics.

Results: GPT-4o (Few-Shot) achieved the highest accuracy and comprehensiveness scores from pathologists, while Gemini demonstrated the best understandability. Patient and clinician understandability ratings were consistently high across models. Mean Reading Grade Level scores varied between internal and external datasets, with GPT-4o Few-Shot performing best overall. BERT-based semantic similarity scores demonstrated distinct trends across models, reflecting differences in text simplification strategies.

Conclusion: LLMs adopt distinct trade-off strategies between simplifying pathology reports and preserving their structure and logic, influenced by prompt design and textual style. Their application shows potential to enhance patient comprehension and clinical communication. Future work should focus on domain-specific fine-tuning to ensure safe and reliable clinical integration.

Background: Preoperative differentiation of precursor glandular lesions (PGL), minimally invasive (MIA), and invasive adenocarcinoma (IAC) in stage IA lung adenocarcinoma (LUAD) is critical for surgical planning but remains challenging due to overlapping CT features and interobserver variability. While existing artificial intelligence (AI) models focus predominantly on binary classification with limited multicenter validation, this study developed and validated a ternary classification framework using pretrained TabPFN and traditional machine learning (ML) algorithms based on AI-derived histogram features, benchmarking against intraoperative frozen section analysis.

Materials and methods: This multicenter retrospective study utilized preoperative CT scans from three institutions between September 2014 and October 2023. Data were divided into training, internal validation, and external test sets. Histogram features (n = 26) were automatically extracted using a commercial AI system (InferRead CT Lung). TabPFN and five ML algorithms were trained with selected clinical and histogram features. Performance was evaluated by accuracy, macro-AUC, sensitivity, specificity, and Cohen's Kappa. Statistical comparisons included DeLong tests for AUC and chi-square for categorical variables.

Results: The cohort comprised 584 stage IA LUAD patients (mean age 57.9 ± 11.0 years; 386 female), divided into training/validation sets (n = 412, center 1) and external test sets (n = 114, center 2; n = 58, center 3). TabPFN achieved macro-AUC of 0.781-0.911 and accuracy of 67.2-78.9% across external test sets, outperforming other ML algorithms. Of note, TabPFN achieved an overall better prediction accuracy compared to frozen section analysis on all test sets (internal: 92.3% vs 84.6%, P = 0.503; external 1: 87.5% vs 75%, P = 1.000; external 2: 67.2% vs 43.1%, P < 0.001). Subgroup analysis revealed superior performance for mGGN lesions (85%) on both external test sets.

Conclusions: TabPFN enables robust, generalizable ternary classification of LUAD subtypes, surpassing conventional ML and frozen section analysis. Its integration with automated histogram analysis offers a scalable solution for preoperative stratification of early-stage lung cancer.

Background: The obesity paradox - where overweight and mild obesity appear associated with improved survival in some cardiometabolic conditions - challenges clinical risk stratification. The inability of body mass index (BMI) to discriminate lean mass from fat, or visceral from subcutaneous adipose tissue, may underlie this paradox.

Methods: To evaluate whether direct measures of abdominal fat distribution and metabolic health status more strongly associate with cardiometabolic morbidity and mortality than BMI alone, and whether they clarify the observed obesity paradox. This cross-sectional analysis with prospective mortality follow-up included 15 925 adults from the 2011-2018 U.S. National Health and Nutrition Examination Survey (NHANES), a nationally representative cohort. Data analysis was performed from July 2025 to October 2025.

Results: Among 15 925 participants, higher VFI and VFI/SFI ratio were consistently associated with increased mortality, whereas higher SFI was protective. After full adjustment, the highest quartile of VFI was associated with significantly increased risk of all-cause [hazard ratio (HR), 1.67; 95% CI, 1.19-2.33] and cardiometabolic mortality (HR, 2.92; 95% CI, 1.44-5.93). The VFI/SFI ratio showed similarly strong associations (all-cause mortality HR, 1.75; 95% CI, 1.19-2.58; cardiometabolic mortality HR, 2.90; 95% CI, 1.42-5.90). In contrast, overweight and obesity class I showed a lower risk of all-cause mortality compared to normal weight (overweight HR, 0.74; 95% CI, 0.56-0.99). Fat distribution indices demonstrated a strong association with CMD incidence in older adult (≥45 years). Metabolically unhealthy status was also a significant mortality risk factor, particularly in females.

Conclusions: In this cross-sectional study of U.S. adults, visceral fat accumulation and an unfavorable fat distribution ratio were more strongly associated with mortality and CMD risk than BMI alone. The apparent survival advantage of elevated BMI was attenuated after accounting for fat distribution and metabolic health. These findings suggest that clinical assessment of obesity-related risk should incorporate measures of fat distribution and metabolic phenotype beyond BMI.