Gland surgery最新文献

Background: Chronic kidney disease (CKD), especially end-stage renal disease (ESRD), is the most common cause of secondary hyperparathyroidism (SHPT), and SHPT is the most severe complication of ESRD. This study aimed to analyze the influencing factors of cardiovascular and fracture events in patients with SHPT which are the leading causes of death in patients with CKD, and provide a reference for selecting patients for whom surgery is more suitable.

Methods: Patients who underwent parathyroidectomy (PTX) for SHPT at The First Affiliated Hospital, Zhejiang University School of Medicine from September 2021 to April 2024 were selected as the study object, with the inclusion and exclusion criteria as followed. They were divided into rural and urban residents for comparison as a cross-sectional study. The study evaluated the comorbidities, socioeconomic status, and postoperative complications diagnosed by radiography of patients undergoing surgery for SHPT.

Results: A total of 119 patients were included, among whom, 71 were from rural areas and 48 were from urban areas. Compared with urban residents, rural residents had poorer economic conditions, a longer interval from disease onset to PTX, and a higher incidence of cardiovascular and fracture events and concurrent nephrolithiasis, all of which were statistically significant. Multivariate analysis indicated that place of residence, age, and duration of uremia were independent risk factors of cardiovascular/fracture events.

Conclusions: Medical staff in ESRD outpatient clinics should pay attention to patients with SHPT. ESRD patients should have better surveillance especially for rural, elder and poor phosphorus control patients, and promptly assess surgical intervention measures.

Background: Breast cancer is a common and complex disease, with various clinical features affecting prognosis. Accurate prediction of prognosis is essential for guiding personalized treatment strategies. This study aimed to develop machine learning models for predicting prognosis in breast cancer patients using retrospective data.

Methods: A total of 6,477 patients from Affiliated Sir Run Run Shaw Hospital were included, and their electronic medical records (EMRs) were thoroughly examined to identify 15 clinical features significantly associated with breast cancer survival. We employed eight different machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop and evaluate the predictive performance of the models. In addition, to investigate the sensitivity of different training/testing set radio to model performance, we examined five sets of ratios: 50:50, 60:40, 70:30, 80:20, 90:10.

Results: Among these models, XGBoost demonstrated the highest performance with receiver operating characteristic (ROC) area under the curve (AUC) of 0.813, accuracy of 0.739, sensitivity of 0.815, and specificity of 0.735. Further statistical analysis identified several significant predictors of prognosis, including age, tumor size, lymph node status, and hormone receptor status. The XGBoost model was found to exhibit superior predictive power compared to established prognostic models such as the Nottingham Prognostic Index (NPI) and Predict Breast. Based on the successful performance of the XGBoost model, we developed a prognosis prediction tool specifically designed for breast cancer, providing valuable insights to clinicians, and aiding them in making informed treatment decisions tailored to individual patients.

Conclusions: Our study highlights the potential of machine learning models in accurately predicting prognosis for breast cancer patients, ultimately facilitating personalized treatment strategies. Further research and validation are warranted to fully integrate these models into clinical practice.

Background: Thyroid cancer is prone to early lymph node metastasis (LNM), and patients with large volume LNM (LVLNM) tend to have a poorer prognosis. The aim of this study was to predict LVLNM in before surgery based on radiomics and deep learning (DL).

Methods: A multicenter retrospective study was performed, including 854 papillary thyroid carcinoma (PTC) patients from three centers. Radiomics features were extracted. Logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), ExtraTrees, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms were used to construct radiomics models. AlexNet, DenseNet121, inception_v3, ResNet50, and transformer algorithms were used to construct DL models. The receiver operating characteristic (ROC) curve was employed to select the better-performing model. A combined model was then created by merging radiomics features and DL features. The least absolute shrinkage and selection operator (LASSO) method was utilized to identify metabolites and radiomics features with non-zero coefficients. The performance of the models was evaluated using area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and F1-score.

Results: A total of 1,357 radiomics features were extracted. Among the radiomics models, the ExtraTrees model demonstrated the optimal diagnostic capabilities with an AUC of 0.787 [95% confidence interval (CI): 0.715-0.858], and DenseNet121 DL model demonstrated the optimal diagnostic capabilities with an AUC of 0.766 (95% CI: 0.683-0.848). Furthermore, the combined model, named the Thy-DL-Radiomics model, exhibited an AUC of 0.839 (95% CI: 0.758-0.920) in the internal validation set and 0.789 (95% CI: 0.718-0.859) in the external validation set.

Conclusions: A radiomics-DL features integrated model can predict LVLNM in PTC patients and provide guidance for personalized treatment.

Background: At present, pancreaticoduodenectomy (PD) is a classic surgical treatment for benign and malignant tumors around ampulla. The operation is complicated and postoperative complications are frequent. Biliary fistula is the most common anastomotic fistula after pancreatic fistula. Our objective is to analyze the risk factors for biliary fistula after PD and to construct a nomogram to predict biliary fistula after PD.

Methods: The clinical data of a total of 196 patients who underwent PD from March 2014 to March 2024 in Yueyang Hospital Affiliated to Hunan Normal University and The Third Xiangya Hospital of Central South University were retrospectively analyzed. The number of included patients was divided in the ratio of 7:3 using the random split method, with 130 patients in the training set and 66 patients in the validation set. Predictors were screened and a nomogram prediction model was constructed by one-way logistic regression analysis, Lasso regression analysis and multifactorial logistic regression analysis. The discriminative ability, consistency and clinical effectiveness of the models were assessed by area under the curve (AUC) of the working characteristics of the subjects, calibration curve and decision curve analysis (DCA).

Results: The results of multifactorial logistic regression analysis showed that diabetes, low albumin, postoperative gastroparesis, abdominal bleeding, abdominal infection, and postoperative pancreatic fistula were the independent risk factors for biliary fistula after PD (P<0.05). The AUC of the column-line graph prediction model constructed from the above factors was 0.807 [95% confidence interval (CI): 0.652-0.962] in the training set and 0.782 (95% CI: 0.517-1.000) in the validation set, suggesting that the diagnostic efficacy of the model was better, and the calibration curves plotted in the training and validation sets were closer to the standard curves, suggesting that the model consistency was better. The plotted DCA curves also indicated a significant positive net gain.

Conclusions: The nomogram prediction model constructed by diabetes, albumin, postoperative gastroparesis, abdominal bleeding, abdominal infection, and postoperative pancreatic fistula can well identify high-risk patients with postoperative biliary fistula (POBF) in PD, which has a good clinical application value.

Background: Iatrogenic bile duct injuries (BDIs) prevention during laparoscopic cholecystectomy (LC) relies on meticulous anatomical dissections through direct visualization. Near-infrared fluorescence (NIRF) with indocyanine green (ICG) improves the visualization of extrahepatic biliary structures. Although ICG can be administered either intravenously or intragallbladder, there remains uncertainty regarding the optimal method for different patient populations. This study sought to assess the suitability of each method for specific patient groups.

Methods: Between October 2021 and May 2022, 59 consecutive patients underwent fluorescence-guided LC at West China Hospital of Sichuan University. Among them, 32 patients received an intravenous injection of ICG (10 mg) 10 to 12 hours prior to surgery (Group A: the intravenous group), while 27 patients received an intragallbladder injection of ICG (10 mg) (Group B: the intragallbladder group). Baseline clinical factors, inclusion criteria, and measurements of parameters and complications were assessed. Data were retrospectively collected and analyzed to evaluate the comparability of the two groups and the clinical outcomes.

Results: Groups A and B included 32 patients (18 males, 14 females), and 27 patients (13 men, 14 women), respectively. In our statistical analysis, significant differences were observed in preoperative diagnoses between the two groups (P=0.041), but the majority of other baseline clinical factors were comparable. Notably, no statistically significant differences were found in complication rates. However, Group A had a shorter operative time (60.38±9.35 vs. 66.78±9.88 min, P=0.01) and superior bile duct fluorescence (P=0.04) than Group B. Interestingly, fluorescence was not observed in impacted gallbladder stones in Group B. Additionally, patients with cirrhosis (P=0.008) and fatty liver (P=0.005) in Group B had higher common bile duct-to-liver ratios (BLRs) than those in Group A.

Conclusions: ICG fluorescence cholangiography allows to visualize extrahepatic biliary anatomical structures with both administration methods. However, the efficacy of bile duct fluorescence varies with different administration routes in diverse patient populations. Hence, appropriate administration route selection for ICG should be tailored to individual patients.

Background: The 8th edition of the American Joint Committee on Cancer (AJCC)'s T-stage for differentiated thyroid cancer (DTC) removes minimal extrathyroidal extension (mETE), while ignoring the risk of mETE would lead to overtreatment or inadequate treatment. The aim of this study was to investigate the impact of location and size of mETE on lymph node metastasis in papillary thyroid cancer (PTC).

Methods: A retrospective analysis of 267 patients who underwent unilateral radical surgery for PTC was conducted. According to the postoperative pathology, they were divided into mETE group (121 patients) and non-mETE group (146 patients). The number of lymph nodes dissected and the number of lymph nodes metastasized were compared between the two groups. The linear regression analysis and the receiver operating characteristic (ROC) curves were performed to evaluate the impact of the locations and sizes on lymph node metastasis.

Results: There was no significant difference in the number of lymph node dissected between the mETE group and the non-mETE group. The tumor located at the upper part and the size <1.0 cm in mETE group showed a higher number of lymph node metastasis (0.78±0.88 vs. 0.25±0.45, P=0.03). Meanwhile, in the mETE group, the number of patients with lymph node metastasis was higher than that in the non-mETE group. Further subgroup analysis revealed that for PTC patients with tumors at the upper part and size <1.0 cm, the number of those with lymph node metastasis in the mETE group was also greater than that in the non-mETE group. Furthermore, the Spearman correlation analysis showed a positive correlation between tumors located at the upper part with a size <1.0 cm and lymph node metastasis rate (R=0.647, P=0.004). Additionally, if the upper part tumor was within 1 cm, the tumor's size was able to identify the lymph node metastasis, with the optimal cut-off point of 0.45 cm (Youden index =0.650).

Conclusions: When tumors combine with mETE, the probability of lymph node metastasis increases in tumors located at the upper part with a size <1.0 cm. Especially, when the upper part tumor is within 1 cm, the tumors of size ≥0.45 cm are more likely to have lymph node metastasis.

Background: Preoperative risk assessment of clinically relevant postoperative pancreatic fistula (CR-POPF) is still lacking. This study aimed to develop and validate a combined model based on radiomics, pancreatic duct diameter, and body composition analysis for the prediction of CR-POPF in patients undergoing pancreaticoduodenectomy (PD).

Methods: Multivariable logistic regression was used to construct a combined model in conjunction with radiomics score (Rad-score), pancreatic duct diameter, and visceral fat area/total abdominal muscle area index (VFA/TAMAI). The models were internally validated using 1,000 bootstrap resamples. The predictive performance of these models was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: The preoperative combined model was validated by 1,000 bootstrap resampling with the area under the ROC curve (AUC) of 0.839 (95% confidence interval: 0.757-0.907). The calibration curves and DCA showed that the combined model outperformed the clinical model and radiomics model. The combined model was presented as a web-based calculator (https://whyyjyljz.shinyapps.io/DynNomapp/).

Conclusions: We explored a method of combining radiomics features, pancreatic duct diameter, and body composition analysis predictors in preoperative assessment for risk of CR-POPF and developed a combined model that showed relatively good performance, but future studies with a larger sample size are needed to verify the stability and generalizability of this model.