Gland surgery最新文献

Background: Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy, and total thyroidectomy is frequently recommended for patients with bilateral disease, large tumors, extrathyroidal extension (ETE), or lymph node metastasis. Hypoparathyroidism is one of the most clinically significant complications after total thyroidectomy, resulting from inadvertent parathyroid gland (PG) injury, devascularization, or removal. Permanent hypoparathyroidism can lead to chronic hypocalcemia, neuromuscular symptoms, renal impairment, and impaired quality of life. While many studies have investigated risk factors for hypoparathyroidism in general thyroidectomy populations, few have focused specifically on oncological characteristics that predict permanent hypoparathyroidism among PTC patients. This study aimed to evaluate oncological predictors of permanent hypoparathyroidism following total thyroidectomy for PTC in a Chinese population.

Methods: A retrospective cohort study was conducted, including 367 patients with postoperative histological confirmation of PTC who underwent total thyroidectomy at a tertiary center in China between January 2017 and January 2021. Clinical, surgical, and pathological parameters were collected. Hypoparathyroidism was defined as low serum calcium with suppressed parathyroid hormone (PTH), and permanent hypoparathyroidism was defined as persistence beyond 6 months. Univariate analyses were performed to screen potential risk factors, and variables with P<0.1 were included in multivariate logistic regression to identify independent predictors.

Results: Permanent hypoparathyroidism developed in 27 patients (7.36%). In univariate analysis, ETE, tumor size, number of involved central lymph nodes (CLNs), and presence of parathyroid tissue in pathological specimens were associated with permanent hypoparathyroidism. Multivariate logistic regression demonstrated three independent predictors: gross ETE [odds ratio (OR) =3.584, P=0.02], presence of parathyroid tissue in pathological specimens (OR =3.809, P=0.005), and a higher number of involved CLNs (OR =1.147, P=0.049). These findings suggest that tumor aggressiveness and surgical complexity contribute to long-term parathyroid dysfunction.

Conclusions: Tumor invasiveness and surgical-related factors significantly contribute to the risk of permanent hypoparathyroidism after total thyroidectomy in PTC patients. Particular attention should be paid to preserving PGs during extensive resection in cases of gross ETE and heavy CLN involvement.

Background: The lung is the most vulnerable site for distant thyroid cancer (TC) metastasis, and individuals who have TC lung metastases (TCLMs) succumb to the illness shortly after diagnosis. This study aims to identify the risk factors of early mortality in TCLM patients and develop a reliable and accurate prediction model. An accurate nomogram for predicting early mortality (survival time ≤3 months) in TCLM patients is necessary.

Methods: Between 2010 and 2015, information gathered from TCLM patients in the Surveillance, Epidemiology, and End Results (SEER) database was used to develop and internally evaluate a prediction model. External validation was performed using data acquired from a Chinese population. All-cause early death (ACED) encompassed mortality from any cause within this period, whereas cancer-specific early death (CSED) specifically referred to deaths explicitly attributed to TC or its complications on the death certificate. The risk factors for CSED and ACED were identified independently using univariate and multivariable logistic regressions. The nomogram's accuracy was confirmed via receiver operating characteristic (ROC) curve analysis, and calibration curves were used to evaluate the consistency between the model predictions and the actual outcomes. Decision curve analysis (DCA) was performed to assess the model's clinical applicability.

Results: This study included 945 patients, 636 (67.3%) of whom died shortly after diagnosis and 335 (35.4%) of whom died from TCLM-related complications. Multivariable logistic regression analyses independently identified six predictors for ACED and seven predictors for CSED. The areas under the curve (AUCs) of the nomogram for predicting ACED and CSED were 0.912 [95% confidence interval (CI): 0.889-0.931] and 0.732 (95% CI: 0.691-0.776), respectively. Combined with the results of the calibration curve analysis, these findings demonstrated that the nomograms effectively predicted the risk of early death in both the internal and external sets. DCA revealed that the nomograms provide considerable clinical advantages.

Conclusions: In the present study, nomograms were developed to reliably predict the risk of early mortality in individuals with TCLM. These tools can assist physicians in identifying high-risk patients and implementing tailored treatment plans as soon as possible.

Background: It has been reported that medullary thyroid cancer (MTC) with distant metastasis may result in poor prognosis. The aim of the study was to estimate the risk of distant metastasis in patients with MTC based on metastasis number and ratio of regional lymph nodes, more than just according to the 8th edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system.

Methods: This cross-sectional study involved 744 participants from the Surveillance Epidemiology and End Results (SEER) STAT 8.4.3 database from 2018 to 2022. Multivariate logistic regression was performed to determine the predictive value of the condition of positive regional lymph nodes for identifying distant metastasis. The estimates are presented as odds ratios (ORs) with 95% confidence intervals (CIs).

Results: In patients with MTC, the 8th edition of the AJCC node (N) stage could traditionally predict the risk of distant metastasis, with N1a (OR 6.21; 95% CI: 1.46-42.42; P=0.03), and N1b (OR 23.20; 95% CI: 6.69-146.54; P<0.001), compared with the stage of N0. However, the upward trend was relatively more significant in the model with the number of metastatic regional lymph nodes. The number of positive regional lymph nodes could predict the risk of distant metastasis, with 1-10 (OR 9.13; 95% CI: 2.47-59.11; P=0.004), 11-20 (OR 25.72; 95% CI: 6.52-171.29; P<0.001), and >20 (OR 26.44; 95% CI: 6.50-178.95; P<0.001) metastatic regional lymph nodes, compared with no metastasis of regional lymph nodes. Similar results could be found in predicting bone metastasis. The cut-off value of metastatic regional lymph node ratio metastatic regional lymph nodes in predicting distant metastasis was 0.176. Patients with the ratio greater than cut-off value were found to have a significantly higher risk of developing distant metastasis (OR 18.24; 95% CI: 6.33-77.26; P<0.001).

Conclusions: Metastasis number and ratio of regional lymph nodes can be effective predictors for distant metastasis in patients with MTC, which is helpful for the modification of the 8th edition of the AJCC N stage.

[This corrects the article DOI: 10.21037/gs-23-493.].

Background: Extrathyroidal extension (ETE) and lymph node metastasis (LNM) are significant factors influencing the prognosis of papillary thyroid carcinoma (PTC). However, their relationship remains controversial. This study explores the connection between ETE and LNM by using propensity score matching (PSM) to guide individualized treatment.

Methods: A retrospective analysis was conducted on 1,045 PTC patients who underwent surgery between January 2023 and June 2024. PSM at a 1:1 ratio was used to balance confounding factors based on univariate and multivariate analyses to investigate the relationship between ETE and LNM.

Results: Among the 1,045 patients, 55.8% had LNM, and 16.1% had ETE. Univariate analysis showed that male sex, age <45 years, tumor size ≥8 mm, ETE, and multifocal were associated with LNM (P<0.05). Multivariate analyses identified male sex, age <45 years, tumor size ≥8 mm, and multifocal as independent risk factors for LNM (P<0.05). After PSM in the present data set, the difference in LNM rates between ETE and non-ETE groups did not reach statistical significance (P>0.05). Similarly, the relationship between LNM and ETE was analyzed. Univariate analysis showed that age <45 years, tumor location, tumor diameter ≥8 mm, multifocal and LNM were risk factors for ETE (P<0.05). Multivariate analysis indicated that age <45 years, tumor located at the isthmus, tumor diameter ≥8 mm and LNM were independent risk factors for ETE (P<0.05). After PSM, no significant difference in ETE was found between patients with and without LNM (P>0.05).

Conclusions: In this single-center, retrospective PSM cohort, we did not observe a significant association between the extent of ETE and LNM in patients with PTC. ETE does not appear to be a reliable indicator for guiding the extent of lymph node dissection. For patients with concurrent ETE, the lymph node dissection range should be personalized.

Background: Invasive ductal carcinoma (IDC) is the most common histological subtype of breast cancer, and axillary lymph node metastasis (ALNM) is a pivotal factor in clinical staging, prognostic assessment, and treatment planning. This study aims to develop and validate a deep learning (DL) model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the prediction of ALNM in IDC patients.

Methods: This multicenter study conducted a retrospective analysis of DCE-MRI images from 520 patients diagnosed with IDC of the breast. The training and internal validation sets consisted of 411 patients from The First Hospital of Qinhuangdao, while the external testing set included 109 patients from the Maternal and Child Health Hospital of Qinhuangdao. Radiomics and DL features were extracted separately from the DCE-MRI images. We evaluated five models (Clinical, Radiomics, Radiomics-Clinical, DL, DL-Clinical) using radiomics features, DL features, and clinical features. Finally, the predictive performance of the models was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC).

Results: The AUCs for the Clinical model and Radiomics model, which are machine learning models, and the DL-model, were 0.807, 0.840, and 0.865, respectively. The combined models incorporating clinical features, namely the Radiomics-Clinical and DL-Clinical models, achieved AUCs of 0.824 and 0.935, respectively. Among the five models, the DL-Clinical model demonstrated a significant advantage in predicting ALNM. Additionally, this model exhibited robust performance in both internal validation and external testing sets, with AUCs of 0.946 and 0.951, respectively.

Conclusions: The DCE-MRI-based DL-Clinical model provides a non-invasive adjunct tool for preoperative identification of ALNM in patients with breast IDC, thereby enhancing the efficacy of personalized treatment strategies and improving patient quality of life.

Background: A subset of breast cancer patients who achieved pathological complete response (pCR) after neoadjuvant therapy (NAT) still experience poor outcomes, including recurrence, metastasis, and death. This study aims to identify risk factors for adverse outcomes in pCR patients, construct predictive models, elucidate molecular subtype-specific prognostic determinants, and explore the peaks of death and progression events among different subtypes.

Methods: Female patients who received NAT and achieved pCR in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled in this research. This study aims to clarify independent prognostic factors of overall survival (OS) and event-free survival (EFS) by using Cox regression analyses as well as developing nomograms and random survival forest (RSF) machine learning model to predict prognoses of patients with pCR. Subgroup analysis was performed to clarify molecular subtype heterogeneity, and survival sequential analysis was conducted to identify survival and progression event peaks.

Results: Analyses based on SEER data identified age, T stage, N stage, molecular subtype, histological tumor type, surgical approach, and histological grade as independent predictors of OS [Concordance index (C-index) =0.723; 3-year area under the curve (AUC) =0.707], while EFS predictors included age, T stage, N stage, molecular subtype, histological tumor type, and grade (C-index =0.682; 3-year AUC =0.690). The C-index of OS and EFS nomograms were 0.723 (3-year AUC =0.711) and 0.682 (3-year AUC =0.691) respectively. The RSF model for mortality risk achieved a C-index of 0.721 (3-year AUC =0.73). Prognostic factors varied across molecular subtypes, though T/N stage was a common determinant. Survival sequential peaks for death events occurred at 36 months [triple-negative breast cancer (TNBC)], 114 months (Luminal), and 97 months [human epidermal growth factor receptor 2 (HER2)-positive subtype], while progression events' peaks were observed at 111 months (TNBC), 114 months (Luminal), and 84 months (HER2-positive subtype).

Conclusions: This study systematically revealed key clinicopathological factors influencing prognosis of pCR patients receiving NAT: tumor burden (T/N stage) emerged as a universal risk factor across molecular subtypes. Survival sequential analysis highlights subtype-specific surveillance priorities: intensified monitoring within 3 years for TNBC, focused follow-up at 7-8 years for HER2-positive subtype, and extended tracking for Luminal subtypes. Both nomograms and the RSF model demonstrated robust predictive performance, providing theoretical and practical tools for precision prognosis management in breast cancer.

Background: Thyroidectomy is a primary treatment for thyroid diseases, with low mortality but a 3-5% complication rate. Delayed tracheal rupture, though rare, is a life-threatening complication causing severe respiratory compromise and mediastinal infections. This case report of three post-thyroidectomy delayed tracheal ruptures shares clinical experiences to improve recognition, management, and preventive strategies.

Case descriptions: Case 1: A 47-year-old male presented on postoperative day (POD) 5 with dyspnea and subcutaneous emphysema. Computed tomography (CT) confirmed tracheal wall disruption, which was managed surgically with muscle flap packing and prolonged drainage. Case 2: A 53-year-old female developed an irritating cough on POD 9. Imaging revealed tracheal cartilage defects, which were repaired via rotational muscle flap. Case 3: A 54-year-old female experienced rapid-onset stridor and septic shock on POD 2. Despite repeated interventions (thoracostomy, intensive care, and anti-infective therapy), she developed progressive pneumomediastinum and two tracheal fistulae, ultimately requiring surgical re-exploration and prolonged ventilator support. All cases required multidisciplinary management, with varying recovery timelines and outcomes.

Conclusions: Delayed tracheal necrosis carries significant morbidity and mortality risks. Prevention hinges on meticulous preoperative evaluation, intraoperative avoidance of tracheal vascular compromise and thermal injury, and heightened postoperative vigilance for warning signs like dyspnea or subcutaneous emphysema. Management should be tailored to severity, ranging from conservative measures to urgent surgical repair. Early multidisciplinary intervention, including aggressive infection control and airway stabilization, is critical to optimize outcomes in this high-stakes complication.