Gland surgery最新文献

Background: Prognostic evaluation of metastatic breast cancer (MBC) currently confronts a two-fold challenge: suboptimal accuracy of conventional scoring systems and insufficient clinical interpretability of machine learning models. This study aimed to construct and validate an accurate prognostic model for predicting the overall survival (OS) of patients with MBC in the Surveillance, Epidemiology, and End Results (SEER) database using machine learning (ML) techniques.

Methods: A total of 1,385 MBC patients were enrolled from the SEER database and randomly assigned into the training cohort (1,035 cases) and the internal validation cohort (350 cases). An external validation cohort comprising 73 patients from Jiaxing Women and Children's Hospital was also set up. The key characteristics influencing the OS were identified through multivariate Cox regression analysis, and prognostic models were constructed using four ML algorithms.

Results: The random survival forest (RSF) model achieved the best performance both in the training and internal validation cohorts, with a concordance index (C-index) of 0.723 [95% confidence interval (CI): 0.704-0.740] and 0.727 (95% CI: 0.693-0.761), respectively. Notably, the area under the curve and Brier scores of the RSF model exceeded those of other models, confirming its superior survival prediction performance. The decision curve analysis (DCA) further indicated that the RSF model could effectively predict the 1-, 3-, and 5-year OS, making it ideal for clinical application. In the external validation cohort, the C-index of the RSF model was 0.685 (95% CI: 0.606-0.758), which, although slightly lower compared with that recorded in the training cohort, was more stable. The area under the curve and Brier scores further confirmed high accuracy and calibration power of the model. The SHapley Additive exPlanations (SHAP) analysis revealed that triple-negative breast cancer (TNBC) and brain metastasis were core variables that increased mortality risk.

Conclusions: The constructed RSF prognostic model demonstrated excellent predictive performance in MBC survival prediction and achieved good interpretability as confirmed by the SHAP analysis. These findings indicate that the developed model can facilitate prognostic assessment and promote the design of individualized treatments for MBC patients.

Background: Cytologically indeterminate thyroid nodules (CITNs) are heterogeneous, and their assessment remains controversial. In this study, we examined the prognostic utility of anti-thyroid antibodies for predicting thyroid cancer (TC) in CITNs.

Methods: This prospective multicenter analysis included all patients with CITNs who were surgically treated between August 2023 and August 2024 at two hospitals in Saudi Arabia; preoperative anti-thyroid antibodies [thyroid peroxidase and thyroglobulin antibodies (TgAbs)], thyroid stimulating hormone (TSH), and a definitive histological diagnosis were required for inclusion. Multivariate analysis was used to identify independent predictors of TC.

Results: This study included 76 individuals; 70/76 (92.1%) were women. Overall, 43/76 (56.6%) nodules had a cytological diagnosis of atypia of undetermined significance (AUS) (Bethesda III), whereas 33/76 (43.4%) were follicular neoplasms (FNs) (Bethesda IV). Pathological analysis revealed that 56/76 (73.7%) patients had benign nodules and 20/76 (26.3%) had malignant tumors. The coexistence of elevated levels of both antibodies and TSH demonstrated the highest odds ratio [3.10; 95% confidence interval (CI): 1.14-5.47], signifying a significant correlation with TC (P=0.003). Furthermore, high levels of one antibody along with elevated TSH levels resulted in an odds ratio of 2.25 (95% CI: 1.02-6.44), indicating a significant association with TC (P=0.04). The multivariate analysis identified Bethesda IV cytological diagnoses, the presence of Hashimoto's thyroiditis, and TSH levels >1 mIU/L as independent predictors of TC.

Conclusions: Elevated anti-thyroid antibody and TSH levels were effective predictors of TC in CITNs. These findings may help guide endocrine surgeons managing patients with these nodules.

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.