Gland surgery最新文献

Background: Papillary thyroid carcinoma (PTC) is the most common malignant tumor of the endocrine system, with its incidence increasing annually worldwide. Currently, despite the adoption of various treatment modalities, tumor invasion and metastasis constitute critical factors affecting the long-term survival of PTC patients. Adipocytes participate in regulating the malignant biological characteristics of tumor cells. However, the role of adipocytes in PTC carcinogenesis and progression remains poorly understood. Therefore, this study aims to investigate the role of adipocytes in PTC metastasis.

Methods: 3T3L1-D cells were treated with differentiation medium to induce differentiation into adipocytes. PTC cells were cultured with adipocytes, and the PTC cell invasion ability was examined by transwell assay. The associated mechanisms were further investigated.

Results: Here, we found that the surrounding adipocytes could promote the invasion capacity of PTC cells via FATP2/TR4 signaling, and the inhibition of FATP2 or TR4 expression could reverse the invasion capacity of PTC cells. Furthermore, the preclinical investigation demonstrated that targeting this newly identified signaling with lipofermata (a FATP2-specific inhibitor) suppressed PTC progression.

Conclusions: Together, these findings suggest that adipocytes in the PTC microenvironment may function via FATP2/TR4 signaling to regulate PTC progression, and targeting this newly identified adipocyte/FATP2/TR4 signaling axis may facilitate the development of novel therapeutic strategies for PTC.

Background: Step-down therapy is becoming a critical approach in the treatment of breast cancer in a localized setting. The applicability of sentinel lymph node biopsy (SLNB) in patients with clinically node-negative (cN0) breast cancer following neoadjuvant chemotherapy (NACT) is still a topic of controversy. The objective of this study was to compare a variety of machine learning algorithms to determine the most effective model for predicting sentinel lymph node (SLN) metastasis in cN0 breast cancer following NACT.

Methods: A total of 221 patients with cN0 breast cancer who underwent standardized NACT combined with SLNB at the Affiliated Hospital of Southwest Medical University from January 2017 to January 2025 were included in this retrospective study. Predictive models for the risk of SLN metastasis were created using four machine learning algorithms. The clinical net benefit was compared through decision curve analysis (DCA), and the diagnostic performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. SHapley Additive exPlanations (SHAP) was also implemented to evaluate the model that demonstrated the most optimal performance.

Results: The SLN-negative group contained 186 cases (84.2%), while the SLN-positive group contained 35 cases (15.8%). The logistic regression (LR) model demonstrated superior performance in comparison to the other models in the testing set. The sensitivity, specificity, accuracy, F1 values, and AUC were, respectively, 0.448, 0.947, 0.864, 0.508, and 0.889 [95% confidence interval (CI): 0.886-0.892]. It yielded the greatest net benefit across the majority of threshold ranges in the testing set. The main predictors identified by SHAP analysis were radiological complete response (rCR), lymphovascular invasion, and axillary nodes on ultrasonography.

Conclusions: The LR model developed in this study demonstrates high specificity and can reliably identify patients without SLN metastasis, thereby supporting the exemption of SLNB in low-risk patients with cN0 breast cancer following NACT.

Background: Cancer-related lymphedema (CRL) seriously affects patients' quality of life. Many practice guidelines emphasize the importance of early prevention. However, a significant gap exists between these guidelines and clinical practice. This study aimed to understand the current status of lymphedema management in Chinese hospitals and to provide a basis for promoting standardized practices in lymphedema care.

Methods: A cross-sectional survey was conducted from September to November 2024 using a self-designed questionnaire on lymphedema management. A convenience sample of 45 hospitals nationwide was surveyed.

Results: The survey revealed that 60% of hospitals had established outpatient clinics for lymphedema, while 46.7% routinely implemented manual lymphatic drainage. Additionally, 68.9% of hospitals performed routine high-risk screening or preoperative assessments for lymphedema. Regarding treatment management, the application rate of complete decongestive therapy (CDT) was 88.9%, but 42.2% of hospitals lacked a standardized fee system. Concerning personnel training, 37.8% of hospitals had not conducted lymphedema training.

Conclusions: Challenges in lymphedema management in Chinese hospitals include the limited implementation of standardized care protocols, incomplete fee systems for treatments, and the low coverage of specialized training. Efforts should focus on enhancing medical staff training, establishing standardized care pathways, and improving technical fee structures to advance the standardized development of lymphedema care.

Background: Mucoepidermoid carcinoma (MEC) is a malignant tumor primarily composed of epidermoid, mucous, and intermediate cells. While it predominantly occurs in the salivary glands, MEC can also develop in the lungs, esophagus, breast, and thymus. Thymic MEC is rare, accounting for 2-3% of primary thymic malignancies. The primary clinical characteristic is incidental discovery in asymptomatic individuals. Due to the rarity of thymic MEC, much of the literature consists of case reports, and no standardized treatment protocol exists. Surgical resection is the primary treatment for MEC.

Case description: This report presents a case of thymic MEC identified incidentally during a routine physical examination in an asymptomatic patient. Chest computed tomography (CT) revealed a well-defined, rounded cystic-solid mass measuring approximately 61 mm × 32 mm in the right anterior mediastinum, with mild enhancement. Following comprehensive assessment to rule out surgical contraindications, the mass was resected. Postoperative pathology indicated a low-grade primary MEC, positive for epithelial membrane antigen (EMA), cytokeratin (CK)19, p63, CK7, cluster of differentiation (CD)5, and mucicarmine/Alcian blue (AB)-periodic acid-Schiff (PAS) staining. Ki-67 was ~5%, and fluorescence in situ hybridization (FISH) analysis confirmed mastermind-like transcriptional coactivator 2 (MAML2) gene rearrangement. The patient received no additional treatment.

Conclusions: This report details a rare case of thymic MEC. The diagnosis required pathology and molecular testing. MAML2 rearrangement suggests a better prognosis; further research is needed to elucidate its pathogenesis and improve its treatment.

Background: Prepectoral implant-based breast reconstruction (BR) via single-port endoscopic axillary approach reduces visible scarring and incision-related complications. TILOOP mesh may improve implant support and aesthetics, but its added value remains uncertain. This study evaluated outcomes, complications, and patient satisfaction with mesh use.

Methods: A retrospective cohort of 383 patients (165 mesh vs. 218 no-mesh) undergoing immediate prepectoral BR via single-port endoscopy [2020-2022] was analyzed. Outcomes included surgical parameters, complications at 30 days/12 months, oncological results at 36 months, and Breast Reconstruction Evaluation and Satisfaction Questionnaire (BREAST-Q) satisfaction.

Results: Baseline features were balanced. The mesh group showed slightly lower mean postoperative pain scores and higher breast satisfaction scores; however, these differences did not reach statistical significance (P>0.05). Mesh use was associated with greater blood loss (98.1±62.2 vs. 91.2±42.2 mL, P<0.05) and longer hospitalization (8.4±4.1 vs. 5.6±2.8 days, P<0.05). There were no significant differences in complication rates, psychological well-being, or physical function between groups. Subgroup analysis indicated that patients with larger breast volumes tended to derive greater aesthetic and structural benefit from mesh use, though these findings should be interpreted cautiously.

Conclusions: Mesh-assisted prepectoral reconstruction demonstrated comparable safety outcomes to non-mesh reconstruction, with a trend toward reduced postoperative pain and improved breast satisfaction but without statistically significant differences. Mesh use was associated with higher blood loss and longer hospitalization. Overall, mesh application should be individualized based on breast volume, reconstructive goals, and patient-specific factors.

Background: The oncologic safety of breast-conserving surgery (BCS) compared with mastectomy in women with node-positive breast cancer remains uncertain in the modern era of systemic therapy and radiotherapy (RT). The aim of this study was to clarify the comparative survival and recurrence outcomes of BCS with RT versus mastectomy in women with pathologically confirmed node-positive breast cancer through a systematic review and meta-analysis of randomized controlled trials (RCTs).

Methods: RCTs enrolling women with pathologically confirmed node-positive breast cancer and directly comparing BCS plus RT with mastectomy were systematically identified. Searches of PubMed, Embase, Web of Science, and Cochrane CENTRAL were conducted through May 2025, following PRISMA 2020 guidelines. Hazard ratios (HRs) for overall survival (OS), disease-free survival (DFS), and breast cancer-specific survival (BCSS), and risk ratios (RRs) for locoregional recurrence (LRR) were pooled using random-effects models. Risk of bias was assessed with the Cochrane RoB 2.0 tool, and sensitivity analyses were conducted to evaluate robustness across trial eras and locoregional treatment strategies.

Results: Ten RCTs comprising 11,714 node-positive patients were included. All BCS patients received whole-breast irradiation (WBI), and nearly all participants received systemic therapy appropriate to tumor biology. Pooled analyses showed no significant differences between BCS and mastectomy for OS [HR 0.94, 95% confidence interval (CI): 0.81-1.07], DFS (HR 1.03, 95% CI: 0.93-1.12), BCSS (HR 1.00, 95% CI: 0.81-1.18), or LRR (RR 0.98, 95% CI: 0.81-1.16). Heterogeneity was negligible (I²=0% for all endpoints). Sensitivity analyses-including exclusion of individual studies, restriction to low-risk-of-bias trials, and stratification by trial era-confirmed the stability of results. Variations in axillary surgery (sentinel lymph node biopsy vs. axillary lymph node dissection) and RT fields (WBI alone vs. WBI + regional nodal irradiation) did not modify comparative outcomes. No publication bias was detected.

Conclusions: BCS with RT provides survival and recurrence outcomes equivalent to mastectomy in women with node-positive breast cancer. These findings support BCS as a safe, patient-centered surgical option in the modern multimodality treatment era and provide a strong evidence base for harmonizing guidelines and reducing unnecessary radical surgery. Future trials incorporating molecular profiling and response-adapted strategies are warranted to refine patient selection.

Background: Category 4 breast cancer lesions with benign and malignant characteristics show substantial overlap in the Breast Imaging Reporting and Data System (BI-RADS). This study aimed to develop and validate a nomogram based on kinetic heterogeneity of the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish benign from malignant BI-RADS 4 breast lesions.

Methods: A total of 271 patients diagnosed with BI-RADS 4 breast lesions by MRI and confirmed by histopathology at the Affiliated Tumor Hospital of Nantong University between January 2018 and June 2023 were retrospectively enrolled. Patients were divided into a benign group (n=84) and a malignant group (n=187) based on postoperative pathological results. The cohort was randomly split into a training set (n=192) and a validation set (n=79) in a 7:3 ratio. Clinical risk factors and MRI features were collected and re-evaluated. Kinetic heterogeneity parameters, including volume, washout component (%), plateau component (%), persistent component (%), predominant peak, and heterogeneity, were extracted using MATLAB and SPM12 software. Statistical analyses compared clinical, imaging, and kinetic parameters between groups. Univariate and multivariate logistic regressions identified independent predictors of malignancy. Three predictive models were constructed: one based on kinetic heterogeneity, one on clinicoradiological features, and a combined model integrating both. A nomogram was developed from the combined model. Model performance was evaluated using receiver operating characteristic (ROC) curves and validated in the independent set. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to assess clinical utility.

Results: Multivariate logistic regression identified peak, heterogeneity, apparent diffusion coefficient (ADC) values, time-intensity curve (TIC) type, and peritumoral edema as independent predictors of malignancy in BI-RADS 4 lesions. Among these, peak, heterogeneity, and ADC values demonstrated strong discriminatory power, with areas under the curve (AUC) of 0.793 [95% confidence interval (CI): 0.723-0.863], 0.816 (95% CI: 0.750-0.881), and 0.773 (95% CI: 0.704-0.842), respectively. The kinetic heterogeneity and clinicoradiological models achieved AUCs of 0.863 and 0.819, respectively. The combined nomogram demonstrated superior diagnostic performance (AUC 0.928 training, 0.906 validation), with high sensitivity and specificity. DCA and CIC confirmed its clinical utility.

Conclusions: The DCE-MRI kinetic heterogeneity-based nomogram is a promising tool to differentiate benign and malignant BI-RADS 4 breast lesions. Prospective external validation is warranted to confirm its potential for improving clinical decision-making and reducing unnecessary biopsies.

Background: Accurate preoperative diagnosis of benign and malignant thyroid nodules is crucial for personalized patient treatment and management. This study aimed to create a dual-source dual-energy computed tomography (DS-DECT) based nomogram model to predict the risk of malignant thyroid nodules.

Methods: A total of 263 patients (288 nodules) with thyroid nodules who underwent preoperative neck DS-DECT scans and were pathologically confirmed were included in this study. The computed tomography (CT) radiological features and DS-DECT-derived quantitative parameters of the nodules were collected. Subsequently, the thyroid nodules were randomly partitioned into a training cohort (n=201) and a validation cohort (n=87) at a ratio of 7:3. Univariate logistic regression analysis identified predictors (P<0.05), followed by least absolute shrinkage and selection operator (LASSO) logistic regression to screen features in the training cohort. Multivariate logistic regression analysis was then conducted to determine independent predictors of malignancy (P<0.05) and to construct a nomogram model for predicting malignancy risk. The performance of the model was assessed using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). The applicability of the nomogram was evaluated through internal validation.

Results: In the final stepwise multivariable logistic regression model, independent predictors of malignancy included iodine concentration in the arterial phase [IC_IAP; odds ratio (OR) =0.286; 95% confidence interval (CI): 0.140-0.517; P<0.001], normalized iodine concentration relative to the thyroid parenchyma in the arterial phase (NIC_P_IAP; OR =0.133; 95% CI: 0.033-0.417; P=0.003), effective atomic number in the venous phase (Zeff_IVP; OR =0.137; 95% CI: 0.050-0.333; P<0.001), thyroid edge interruption (OR =3.791; 95% CI: 1.599-9.491; P=0.003), and enhanced blurring (OR =3.247; 95% CI: 1.373-7.937; P=0.008). The AUCs of the nomogram model, based on these five factors, were 0.932 (95% CI: 0.898-0.963) for the training set and 0.908 (95% CI: 0.842-0.964) for the validation set. The Hosmer-Lemeshow test indicated that the nomogram model had a good fit (P>0.05), and the calibration curve was close to the standard curve. DCA showed significant net benefits from using the model.

Conclusions: The nomogram model, based on normalized multiphase quantitative DECT parameters and qualitative imaging features, serves as an effective diagnostic tool for imaging physicians to distinguish between benign and malignant nodules.