Application of carbon nanoparticles combined with refined extracapsular anatomy in endoscopic thyroidectomy.

Objective: To evaluate the value of refined extracapsular anatomy combined with carbon nanoparticle suspension tracing technology for protecting parathyroid function and the thoroughness of lymph node dissection in the central region during endoscopic thyroid cancer surgery. Patients and methods: Retrospective clinical data analysis was performed on 108 patients who underwent endoscopic thyroid cancer surgery at the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital) from November 2019 to November 2022. Before surgery, thyroid function tests, color Doppler ultrasounds and neck-enhanced CT scans were performed on all patients. Cytopathological diagnosis obtained via ultrasound-guided fine-needle aspiration served as confirmation for the primary diagnosis. It was determined whether to perform a total thyroidectomy or a hemithyroidectomy (HT) together with preventive unilateral (ipsilateral) central neck dissection. Follow-up times were 1 to 34 months. Results: Transient neuromuscular symptoms were present in 3.70% (4/108) cases, with no permanent neuromuscular symptoms or permanent hypoparathyroidism. Regarding transient hypoparathyroidism, the patients recovered after three months and did not need long-term calcium supplementation. The number of harvested LNs (mean± SD) was 5.54 ± 3.84, with ≤5 in 57.41% (62/108) and >5 in 42.59% (46/108) cases. The number of patients with metastatic LNs was 37.96% (41/108), with ≤2 in 65.85% (27/41) and >2 in 34.15% (14/41) cases. Conclusions: Fine extracapsular anatomy combined with carbon nanoparticle suspension tracing is effective in endoscopic thyroid cancer surgery. It can improve the thoroughness of prophylactic central neck dissection and recognition of the parathyroid gland and avoid parathyroid injury and other complications to effectively protect parathyroid function.

Preoperative prediction for lymph node metastasis in early gastric cancer by interpretable machine learning models: A multicenter study.

BACKGROUND: The presence of lymph node metastasis plays a decisive role in the selection of treatment options in patients with early gastric cancer. However, there is currently no established protocol to predict the risk of lymph node metastasis before/after endoscopic resection. The aim of this study was to develop and validate several machine learning algorithms for clinical practice. METHODS: A total of 2,348 patients with early gastric cancer were selected from 5 major tertiary medical centers. We applied 6 machine learning algorithms to develop lymph node metastasis prediction models for clinical feature variables. The partial dependence plots were used to explain the prediction of the models. The area under the receiver operating characteristic curve and area under the precision recall curve were measured to assess the detection performance. The R shiny interactive web application was used to translate the prediction model in a clinical setting. RESULTS: The incidence of lymph node metastasis in patients with early gastric cancer was 13.63% (320/2348) and significantly higher in young women, in the lower third of the stomach, with a size >2 cm, depressed type, poorly/nondifferentiated, lymphovascular invasion, nerve invasion, and submucosal infiltration. In terms of age, there is a nonlinear and younger trend. XGBOOST displayed the best predictive performance at the initial and postendoscopy evaluation. In addition, the machine learning algorithm was converted to a user-friendly web tool for patients and clinicians. CONCLUSION: XGBOOST can predict the risk of lymph node metastasis with best accuracy in patients with early gastric cancer. Our online web application may help determine the optimal best surgical option for patients with early gastric cancer.