Gland surgery最新文献

Background: Differentiated thyroid cancer (DTC) patients (tumor size ≤4 cm) with minimal extrathyroidal extension (MEE), which was used to be classified as T3 disease, now are classified as T1/T2 disease according to the largest tumor size. However, few studies explored the survival difference between DTC patients with MEE who received lobectomy or total thyroidectomy treatment. We aimed to compare the long-term survival rate of DTC patients with MEE after total thyroidectomy and lobectomy by using the national cancer registration data.

Methods: We performed a retrospective cohort analysis to examine the long-term survival outcomes after lobectomy versus total thyroidectomy treatment of DTC patients with MEE using the univariate and multivariate survival analysis.

Results: Of 1,889 included DTC patients with MEE, 113 patients (6.0%) received lobectomy and 1,776 patients (94.0%) received total thyroidectomy. DTC patients with MEE who underwent lobectomy experienced a similar cancer-specific survival (CSS) rate compared with those who underwent total thyroidectomy (10-year CSS rate: 99.1% vs. 98.8%, P=0.99). Considering deaths not related to thyroid cancer, the 10-year cumulative incidence of cancer-related death was 0.9% for DTC patients with MEE who underwent lobectomy and 1.2% for those who received total thyroidectomy (P=0.99). After adjusting for potential confounding factors, DTC patients with MEE who received lobectomy experienced a similar risk of death compared to those who underwent total thyroidectomy in both the multivariate Cox regression model [adjusted hazard ratio (HR), 1.99; 95% confidence interval (CI): 0.45-8.80; P=0.36] and the multivariate competing risk regression model [adjusted subdistribution hazard ratio (SHR), 1.99; 95% CI: 0.44-8.89; P=0.37].

Conclusions: pT1/pT2 DTC patients with MEE who underwent lobectomy or total thyroidectomy have excellent comparable survival outcomes, which supports the increased use of lobectomy in the treatment of these patients.

Background: The optimal extent of thyroidectomy for papillary thyroid carcinoma (PTC) measuring >1 cm and ≤4 cm remains a subject of debate. This study aimed to determine the optimal surgical extent for clinically node-negative (cN0) unilateral PTC measuring >1 cm and ≤4 cm and to identify factors associated with recurrence risk.

Methods: We conducted a retrospective analysis of 403 patients with unilateral cN0 PTC measuring 11-40 mm. Propensity score-matched (PSM) analysis was performed using five covariates including age, sex, tumor size, central neck dissection, and follow-up duration. Patients were stratified by tumor size (11-20 vs. 21-40 mm), degree of extrathyroidal extension (ETE) (none, minimal, and invasion into the strap muscle), and surgical extent (lobectomy vs. total thyroidectomy). Recurrence and survival outcomes were compared.

Results: In the baseline cohorts, among the 403 patients, 304 had 11-20 mm tumors, and 99 had 21-40 mm PTC. Total thyroidectomy was performed in 65.3% of cases. Rates of minimal ETE and strap muscle invasion were 41.4% and 8.2%, respectively. Recurrence rates did not differ significantly by tumor size, ETE status (except strap muscle invasion), or surgical extent. Strap muscle invasion independently predicted recurrence [hazard ratio (HR) =6.380, P=0.01] and was associated with poorer disease-free survival. After PSM, 88 pairs of patients were generated in the lobectomy and total thyroidectomy groups. In the PSM cohort, the recurrence rate did not differ between the two groups. However, the overall complication rate was significantly higher in the total thyroidectomy group (49.5% vs. 24.8%, P=0.009), largely driven by a higher rate of transient hypoparathyroidism (39.1% vs. 7.6%, P<0.001).

Conclusions: In patients with unilateral cN0 PTC measuring 11-40 mm, lobectomy may serve as an appropriate primary surgical option, providing disease control comparable to total thyroidectomy while reducing procedure-related complications, except in those with strap muscle invasion.

Background: The prognosis of patients with metastatic (M1) breast cancer is controversial, and the prognostic value of local therapy has not been well established. We aimed to develop and validate explainable machine learning (ML)-based survival models to predict overall survival (OS) in this population.

Methods: We retrospectively identified 10,214 female patients with histologically confirmed M1 breast cancer diagnosed between January 2013 and December 2018 from the Surveillance, Epidemiology, and End Result (SEER) database, each with a single malignant lesion. Patients with ambiguous or incomplete metastasis data were excluded. Candidate predictors included age; sex; laterality; American Joint Committee on Cancer (AJCC) Tumor, Node, and Metastasis stage; surgery of the primary site; breast subtype; estrogen receptor and progesterone receptor status; marital status; radiotherapy; chemotherapy; tumor grade; histology; and metastasis to the bone, brain, liver, and lung. Two time-to-OS prediction models-a neural network and a Cox proportional hazards model-were trained, internally validated, and externally tested in a cohort of 100 patients with M1 breast cancer from China. Model interpretability was assessed through global and individual feature importance analyses.

Results: In total, 10,314 patients were enrolled in the study. The median follow-up time was 42 months in the training dataset and 36 months in the test dataset. The deep learning network demonstrated greater stability and accuracy than did the Cox proportional hazards model in predicting patient survival, both on the internal test dataset (concordance index: 0.771 vs. 0.632) and in the external validation (concordance index: 0.782 and 0.650). Several important prognostic factors were identified by the deep learning model, including breast subtype, metastatic site, and surgery status. Surgery was associated with improved OS in patients with bone metastases selected after propensity score matching, with 5-year OS rates of 76.9% and 27.2% in the surgery and nonsurgery groups, respectively (P=0.001).

Conclusions: We developed and externally validated ML models that accurately predict survival in patients with M1 breast cancer. Breast subtype, metastatic site, and surgery status were the most important factors for survival prediction in this population. Patients with non-triple-negative breast cancer and metastasis to the bone may benefit from surgery, while those with metastasis to the brain, lung, or liver may not.

Background: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer and lymph node metastasis (LNM) significantly impacts patient prognosis. Preoperatively identifying lateral lymph node metastasis (LLNM) presents significant challenges, as current diagnostic techniques, such as ultrasonography, have limited sensitivity and precision. This study aimed to develop and validate a predictive model for LLNM in patients with PTC, using data from the Surveillance, Epidemiology, and End Results (SEER) database and external validation cohorts.

Methods: Data from 18,342 patients with PTC diagnosed from 2016 to 2020 were retrieved from the SEER database. The patients were arbitrarily categorized into training (n=12,839) and validation (n=5,503) cohorts. Both univariate and multivariate logistic regression analyses were conducted to identify the independent risk factors for LLNM. A predictive nomogram was developed based on these factors, and its accuracy was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Results: Five independent predictors of LLNM were identified: sex, age, race, tumor (T) stage, and metastasis (M) stage. The nomogram demonstrated strong predictive performance, with an area under the curve (AUC) of 0.715 [95% confidence interval (CI): 0.706-0.724] in the training cohort and 0.707 (95% CI: 0.693-0.720) in the validation cohort. The calibration curves indicated good agreement between the predicted and actual outcomes, while DCA confirmed the clinical applicability of the model across various risk thresholds.

Conclusions: This study successfully developed a predictive model for LLNM in patients with PTC by integrating demographic and clinicopathological indicators. This model demonstrates significant predictive precision and practical clinical use, assisting medical professionals in identifying high-risk patients and optimizing surgical choices. Further studies incorporating more variables are warranted to improve the diagnostic accuracy of the model.

Background: Enhanced recovery after surgery (ERAS) protocols aim to attenuate the physiological stress of surgery and accelerate postoperative recovery. While widely adopted in several surgical fields, their role in adrenal surgery remains less defined. The aim of this systematic review and meta-analysis was to evaluate the impact of ERAS protocols on perioperative outcomes in patients undergoing adrenalectomy compared with conventional care.

Methods: Eligible studies included randomized controlled trials (RCTs), prospective cohorts, and retrospective comparative analyses involving adult patients undergoing adrenalectomy managed with ERAS versus standard care. A systematic literature search was conducted in PubMed, Scopus, ScienceDirect, and the Cochrane Library, without time restrictions, with the last search performed in August 2025. Primary outcomes were postoperative length of stay (LOS), pain, and complication rates. Secondary outcomes included functional recovery, gastrointestinal recovery, and hospital costs. Risk of bias (RoB) was assessed using the RoB 2.0 tool for RCTs and the ROBINS-I tool for non-randomized studies. Quantitative synthesis was performed using a random-effects meta-analysis, with standardized mean differences (SMDs) and risk ratios used to summarize continuous and dichotomous outcomes, respectively.

Results: Six comparative studies, including a total of 429 patients, met the inclusion criteria. ERAS protocols were associated with a significant reduction in postoperative LOS, postoperative complications, and pain scores. ERAS pathways also resulted in faster functional recovery, including earlier mobilization, urinary catheter removal, and gastrointestinal recovery, without an increase in major complications. Although individual studies reported cost savings with ERAS implementation, pooled analysis did not demonstrate a statistically significant reduction in hospitalization costs.

Conclusions: ERAS protocols in adrenalectomy are associated with improved perioperative outcomes, faster recovery, and reduced postoperative morbidity without compromising safety. However, the available evidence is limited by heterogeneity and methodological quality. Further high-quality prospective studies are needed to develop and validate standardized ERAS guidelines tailored specifically to adrenal surgery.

Background: Human epidermal growth factor receptor 2 (HER2)-negative early-stage breast cancer (BC) exhibits significant heterogeneity, complicating personalized treatment decisions after breast-conserving surgery (BCS). Robust tools integrating baseline risk, treatment response, and sociodemographic factors are needed to optimize survival while minimizing unnecessary toxicity. This study aimed to create a clinical decision-support tool that leverages these multifaceted factors to optimize survival outcomes and minimize treatment toxicity for these patients.

Methods: Utilizing population-level data from the Surveillance, Epidemiology, and End Results (SEER) program (cases from 2010 to 2016; n=8,384), we constructed and validated prognostic nomograms for overall survival (OS) and cancer-specific survival (CSS) in a cohort of HER2-negative, T1-3N0-1 BC patients who underwent BCS followed by radiotherapy. Key prognostic variables were identified through multivariable Cox proportional hazards regression. The performance of the nomograms was rigorously assessed using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Finally, risk stratification was performed by applying optimal cut-off points determined via X-tile software.

Results: Key independent predictors included tumor grade, tumor (T) stage, estrogen receptor (ER)/progesterone receptor (PR) status, marital status, and single primary tumor status. Nomograms significantly outperformed American Joint Committee on Cancer (AJCC) 7th staging (OS C-index: 0.69 vs. 0.63; CSS C-index: 0.74 vs. 0.63). Patients were stratified into low- (33%), middle-, and high-risk (27%) groups. Chemotherapy provided no OS/CSS benefit in low-risk patients but substantially improved outcomes in high-risk patients (P<0.001). Achieving a complete response (CR) following neoadjuvant chemotherapy (NAC) was associated with superior survival outcomes, particularly among high-risk patients, whereas a non-complete response (NCR) was linked to worse survival.

Conclusions: We developed the first validated nomograms integrating tumor biology, treatment response, and social factors to optimize HER2-negative BC management. Identifying 'single primary tumor' status as a novel prognostic indicator point to novel tumorigenesis mechanisms. Critically, our findings enable actionable strategies: low-risk patients (33%) may be candidates for avoiding chemotherapy toxicity, while high-risk patients (27%) are potential candidates for more intensive treatment strategies. Patients who fail to achieve a CR should be considered for enrollment in adjuvant trials with novel agents. Adding prospective biomarkers will further refine these precision approaches.