Gland surgery最新文献

Background: Pigmented lesions of the nipple-areola complex (NAC) are uncommon and may pose a diagnostic challenge because they can mimic malignant melanoma and other NAC conditions. Pigmented basal cell carcinoma (BCC) arising in this sun-protected site is particularly rare and may be overlooked, especially in Asian patients. This case highlights the diagnostic overlap between melanoma-like clinical features and pigmented BCC in the NAC, which can create uncertainty during initial evaluation.

Case description: A 61-year-old Thai woman presented with a 1-year history of a progressively enlarging, asymptomatic hyperpigmented lesion on the left NAC. There was no history of trauma or chronic sun exposure. On examination, the lesion was an irregularly shaped pigmented plaque 1 cm with asymmetry, heterogenous pigmentation localized to the left NAC, raising clinical concern for melanoma based on ABCDE criteria (Asymmetry, Border irregularity, Color variation, Diameter >6 mm); no definite evolution was reported. Mammography and ultrasonography demonstrated focal skin thickening at the lesion site without an underlying breast mass or nodal involvement. Dermoscopic evaluation was not available in our clinical setting and therefore was not performed. A punch biopsy confirmed superficial pigmented BCC. Definitive excision was planned with approximately 4-mm clinical margins; final pathology demonstrated negative margins on all sides, with the closest histologic margin measuring 7 mm. The postoperative course was uneventful, and there was no evidence of recurrence at the 6-month follow-up.

Conclusions: Although rare, pigmented BCC of the NAC should be considered in the differential diagnosis of persistent pigmented NAC lesions that mimic malignant melanoma. Systematic clinical assessment, dermoscopic evaluation when available, and timely biopsy when indicated are important to establish the diagnosis and guide appropriate management. Given the short follow-up duration, longer surveillance is required to assess long-term disease control.

Background: Identifying parathyroid glands during surgery is challenging. Recent studies suggest that near-infrared autofluorescence (NIRAF) detection can improve their recognition. However, the correlation between positive NIRAF detection and clinical features in parathyroidectomy remains unclear. This study aimed to explore correlation between positive NIRAF detection and clinical features in parathyroid surgery, aiming to improve the application of this technology.

Methods: Prospective data were collected from patients undergoing parathyroidectomy using handheld NIRAF devices. Data included the ratio of parathyroid NIRAF to baseline, and clinical features such as age, gender, body mass index (BMI), parathyroid gland size, preoperative blood calcium, parathyroid hormone levels, and pathological properties. Factors influencing parathyroid autofluorescence intensity were evaluated.

Results: From June 2023 to June 2024, 20 primary and 19 secondary hyperparathyroidism patients with 98 parathyroid lesions were analyzed. NIRAF probe detection showed no significant correlation with age, gender, or preoperative serum calcium. However, it was significantly correlated with parathyroid volume, BMI, and pathological properties. Smaller glands, lower BMI, and diffuse parathyroid hyperplasia were more easily detected by near-infrared probes (P<0.01).

Conclusions: NIRAF probes improve the accuracy and safety of parathyroidectomy. Positive NIRAF detection is significantly correlated with parathyroid volume, BMI, and pathology, making it particularly useful for patients with smaller BMI, smaller gland volume, and diffuse hyperplasia.

Background and objective: Autologous breast reconstruction is a complex microsurgical procedure in which perioperative management critically influences surgical and patient-reported outcomes. Several physiological and perioperative strategies-including oxygenation enhancement, metabolic modulation, nutritional support and wound care-remain underexplored in this specific setting. This narrative review summarizes evidence on emerging approaches to perioperative optimization in autologous breast reconstruction and discusses their potential to improve recovery and long-term outcomes.

Methods: A narrative literature review was conducted using PubMed (MEDLINE, Bethesda, MD, USA) and Scopus (Elsevier, Amsterdam, The Netherlands) for studies published in English up to October 2025. Reference lists of key reviews and consensus statements were manually screened to identify additional relevant publications. The selected literature was analyzed thematically to highlight evolving concepts and their reported impact on reconstructive outcomes.

Key content and findings: Emerging strategies target perioperative physiology through systemic and local interventions. Hyperbaric oxygen therapy and closed-incision negative pressure wound therapy demonstrate potential benefits in improving perfusion, reducing edema, and lowering wound-related complications. Antioxidant and metabolic modulation, perioperative protein optimization, and preoperative carbohydrate loading may attenuate catabolic stress and support microvascular stability. Structured drain care and infection-prevention protocols further contribute to safer recovery. Evidence remains heterogeneous and limited in scope, but collectively these approaches point toward a more comprehensive and individualized perioperative management paradigm in reconstructive breast surgery.

Conclusions: Optimizing perioperative care in autologous breast reconstruction requires an integrated framework that aligns surgical precision with systemic support. Emerging biological and metabolic interventions may complement established recovery practices, potentially improving flap outcomes and overall patient well-being. Prospective multicenter studies are needed to standardize protocols and determine their clinical impact.

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.