Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

Objective To investigate the application value of quantitative parameters MRFDGmax and SUVmax in the stages of hepatitis,liver fibrosis and cirrhosis in rats by whole-body dynamic 18 F-FDG PET/CT Patlak imaging.Methods Twenty-four SD rats were randomly divided into four groups of six rats each,which were the normal group,hepatitis group,liver fibrosis group and cirrhosis group.According to the experimental grouping,rats in each group were induced by the CC14 oil solution complex method.Whole-body dynamic 18 F-FDG PET/CT patlak imaging was performed on each group of rats separately at the completion of induction.After the imaging was com-pleted,the MRFDGmax,SUVmax and CT values of the livers of each group were analyzed;subsequently,the serum of rats in each group was extracted for the detection of liver function indexes(AST,ALT and ALP),and HE staining was performed on the livers of rats in the normal,hepatitis and cirrhosis groups,and Masson staining was performed on those in the liver fibrosis group;the α-SMA expression in the liver tissues of each group was analyzed by immu-nohistochemical method.The data were analyzed by one-way ANOVA,two independent samples t-test and Pearson correlation analysis.Results MRFDGmax,SUVmax values were statistically significant differences among normal,hep-atitis,liver fibrosis and cirrhosis groups(F=84.54,38.35,P＜0.001).The difference in CT values between liver fibrosis and cirrhosis groups was not statistically significant(t=-0.407,P=0.693),and the difference was statistically significant when compared between the rest of the groups(F=112.25,P＜0.001).Compared with the normal group,AST,ALT and ALP of the experimental group showed a staged increase,and the differences were statistically significant(F=93.32,64.63,145.03,P＜0.001).HE staining showed that hepatocytes of the normal group were neatly arranged and structurally intact;a large number of inflammatory cells infiltrated the hepa-titis group with steatosis;pseudo lobe formation was observed in the cirrhosis group.Masson staining of the liver fi-brosis group showed collagen fiber proliferation and thickening of the peritoneum.Immunohistochemistry test results showed that α-SMA expression increased in hepatitis group,liver fibrosis group and cirrhosis group,with a staged increase,and the difference was statistically significant(F=80.57,P＜0.001).Correlation analysis showed a positive correlation between SUVmax and MRFDGmax(r=0.967,P＜0.01).α-SMA was positively correlated with AST,ALT and ALP in the hepatitis,liver fibrosis and cirrhosis groups,respectively(r=0.924,0.756,0.934,P＜0.01).Conclusion Whole-body dynamic 18F-FDG PET/CT Patlak imaging has application value in monitoring hepatitis,liver fibrosis and cirrhosis stages through quantitative parameters MRFDGmax and SUVmax changes.

Objective To monitor and evaluate the synergistic antitumor effects of programmed death-1(PD-1)checkpoint inhibitor combined with radiation therapy through whole-body dynamic 18 F-Fluorodeoxy glucose positron emission computed tomography(18F-FDG PET/CT)and Patlak multi-parametric analysis.Methods B16F10 mel-anoma dual-tumor mouse model was established and randomly divided into control,PD-1 monoclonal antibody,ra-diation-only,and combination groups(n=6).Whole-body 18F-FDG PET/CT imaging was performed before and 24 hours post-treatment.The changes of maximum standardized uptake value(SUVmax)and metabolic rate of FDG(MRFDG)changes were analyzed and compared.Mice were then euthanized,tumors excised and underwent histo-pathology with HE,CD8,Ki-67 staining to assess immune infiltration and proliferation.Distal tumor volumes were monitored during treatment.Results At 24 hours post-treatment,in the primary tumors,SUVmax and MRFDG values increased compared to pre-treatment in the control group(P＜0.000 1),while they decreased in the combination treatment group(P＜0.000 1),with statistically significant differences.In the distal tumors,SUVmax and MRFDG values increased compared to pre-treatment in the control group,PD-1 monoclonal antibody group,and radiothera-py-alone group.The SUVmax differences were statistically significant in the control group before and after treatment(P＜0.000 1).MRFDG values in the distal tumors showed statistically significant differences in all three groups(P＜0.01 or P＜0.000 1).In the combination treatment group,SUVmax and MRFDG values in the distal tumors de-creased significantly compared to pre-treatment(P＜0.000 1).Post-treatment comparison of SUVmax and MRFDG values in the distal tumors showed that statistically significant differences in SUVmax and MRFDG values were observed among all groups except between the radiotherapy-alone and PD-1 monoclonal antibody groups(all P＜0.05).Im-munohistochemistry results showed that the mean absorbance value of CD8 T lymphocytes in the distal tumor was significantly higher than that in the other three groups(P＜0.001);the mean absorbance value of Ki-67 immuno-histochemistry in the distal tumor proliferation index was significantly lower than that in the other three groups(P＜0.001).Conclusion The synergistic effects of combined treatment reduced distal tumor growth.Whole-body 18F-FDG PET/CT Patlak multi-parametric imaging can monitor the synergistic effects of PD-1 antibody and radiotherapy in B16F10 melanoma,providing reliable imaging parameters for optimizing combinatorial therapies.

ObjectiveTo investigate the influencing factors for rebleeding after endoscopic therapy and the effect of the number of sequential treatment sessions on postoperative rebleeding in patients with liver cirrhosis receiving secondary prevention of gastroesophageal varices （GOV）. MethodsA total of 1 717 patients with liver cirrhosis who received secondary prevention of GOV and attended The 960th Hospital of the PLA Joint Logistice Support Force from January 2017 to December 2021 were enrolled， and according to the presence or absence of bleeding after endoscopic therapy， they were divided into non-bleeding group and rebleeding group. The influencing factors for rebleeding were analyzed， as well as the association between the number of endoscopic treatment sessions and rebleeding. The chi-square test was used for comparison of categorical data between groups； the independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between the two groups； the Kruskal-Wallis H test was used for comparison bertween multiple groups， and the Wilcoxon test was used for further comparison between two groups. The Cox regression model was used to investigate the influencing factors for rebleeding， and the Kaplan-Meier method was used to plot survival curves， while the Log-rank test was used for comparison between groups. ResultsOf all patients， 286 （16.7%） experienced rebleeding after endoscopic therapy， and 1 431 （83.3%） did not experience bleeding. There were significant differences between the two groups in history of smoking and drinking， etiology of liver cirrhosis， hemoglobin （Hb）， prothrombin time （PT）， prothrombin activity （PTA）， international normalized ratio （INR）， albumin （Alb）， fasting blood glucose， blood urea nitrogen， Child-Pugh class， aspartate aminotransferase-to-platelet ratio index （APRI） score， albumin-bilirubin （ALBI） score， use of non-selective beta-blocker （NSBB） before surgery， treatment modality， type of varices， and maximal varicose vein diameter （all P<0.05）. The univariate Cox regression analysis showed that in the patients with liver cirrhosis who received secondary prevention of GOV， rebleeding was associated with history of smoking and drinking， etiology of liver cirrhosis， use of NSBB before surgery， treatment modality， maximal varicose vein diameter， Hb， platelet count， PT， PTA， INR， Alb， total bilirubin （TBil）， alkaline phosphatase （ALP）， gamma-glutamyl transpeptidase， blood glucose， Child-Pugh class， and ALBI score （all P<0.05）. The multivariate Cox regression analysis showed that Hb （hazard ratio ［HR］=0.989， 95% confidence interval ［CI］： 0.983‍ ‍—‍ ‍0.994， P<0.001）， TBil （HR=1.020， 95%CI： 1.006‍ ‍—‍ ‍1.034， P=0.005）， Alb （HR=0.868， 95%CI： 0.758‍ ‍—‍ ‍0.994， P=0.041）， treatment modality （sclerosing agent： HR=2.158， 95%CI： 1.342‍ ‍—‍ ‍3.470， P=0.002； tissue adhesive： HR=2.709， 95%CI： 1.343‍ ‍—‍ ‍5.462， P=0.005； ligation+sclerosing agent： HR=3.181， 95%CI： 1.522‍ ‍—‍ ‍6.645， P=0.002； sclerosing agent+tissue adhesive： HR=1.851， 95%CI： 1.100‍ ‍—‍ ‍3.113， P=0.020）， ALP （HR=1.003， 95%CI： 1.001‍ ‍—‍ ‍1.004， P=0.002）， and maximal varicose vein diameter （HR=1.346， 95%CI： 1.119‍ ‍—‍ ‍1.618， P=0.002） were independent influencing factors for rebleeding after endoscopic therapy. Comparison of rebleeding rate after different numbers of sequential treatment sessions showed that the patients treated for three sessions had a significantly lower rebleeding rate than those treated for one or two sessions （χ²=8.643 and 5.277， P=0.003 and 0.022）. The survival analysis showed that with the increase in the number of treatment sessions， there was a significantly longer interval between rebleeding （P=0.006） and a significantly lower mortality rate （P<0.001）. ConclusionThe levels of TBil， ALP， Hb， and Alb on admission， endoscopic treatment modality， and maximal varicose vein diameter were the main predictive factors for rebleeding after endoscopic therapy for GOV in liver cirrhosis， and such predictive factors should be closely monitored in clinical practice. Regular endoscopic therapy can reduce the rebleeding and mortality rates of patients with liver cirrhosis and GOV and prolonmg the interval between rebleeding.

Objective To address the difficulty in collecting sufficient endoscopic images of colorectal submucosal tumors for traditional deep learning model training,a few-shot learning based model(FSL model)is proposed for classifying colorectal submucosal tumors and polyps on endoscopic images.Methods A total of 172 endoscopic images of colorectal submucosal tumors were collected from different centers,including 43 each of colorectal lipomas(CRLs),neuroendocrine tumors(NETs),serrated lesions and polyps(SLPs),and traditional adenomas.A support set and a query set were constructed using these endoscopic images.ResNet50 which was pre-trained on ImageNet and esophageal endoscopic images was used to extract image features.Subsequently,K-nearest neighbors algorithm was used for classification based on the calculated Euclidean distance.The classification performance of FSL model was evaluated through the comparison with the original model and endoscopists.Results FSL model had a 4-class classification accuracy of 0.831,Macro AUC of 0.925,Macro F1-score of 0.831;moreover,the proposed model achieved diagnostic accuracies of 0.925 and 0.906 for CRLs and NETs,with F1 score of 0.850 and 0.805.Additionally,the proposed model exhibited high classification consistency(Kappa=0.775)and interpretability.Conclusion The established FSL model performs well in distinguishing CRLs,NETs,SLPs and traditional adenomas on endoscopic images,indicating its potential utility in assisting the identification of colorectal submucosal tumors under endoscopy.

Objective:To investigate the distribution of iron deposition in the substantia nigral (SN) subregions on quantitative susceptibility mapping (QSM) and the change of swallow tail sign (STS) in patients with relapsing-remitting multiple sclerosis (RRMS) of different disease stages.Methods:The clinical and imaging data of 53 patients with RRMS (case group) diagnosed at the First Hospital of Chongqing Medical University from November 2019 to December 2021 were retrospectively analyzed. The case group was divided into 0-5 years subgroup, 6-10 years subgroup, and >10 years subgroup according to the disease duration; another 37 age-and gender-matched healthy volunteers were recruited as the control group during the same period. All subjects underwent MRI and QSM reconstruction. First, the SN was divided into four subregions: rostral anterior-SN (aSNr), rostral posterior-SN (pSNr), caudal anterior-SN (aSNc), and caudal posterior-SN (pSNc) on the QSM, and the quantitative susceptibility value (QSV) of each subregion was measured, and then the STS of the SN was observed and scored on the susceptibility weighted imaging (SWI) generated by post-processing. ANOVA was used to compare the differences in the QSV of each subregion of SN among the groups, and the probability of abnormal STS was compared using the χ 2 test. Spearman′s test was used to analyze the correlation between the QSV of each subregion of SN and the STS score. Results:The differences in QSV of aSNr, pSNr, aSNc, and pSNc were statistically significant among the 0-5 years subgroup, 6-10 years subgroup,>10 years subgroup of RRMS patients and the control group ( P<0.05). The QSV of aSNr, pSNr, and aSNc in 0-5 years subgroup was higher than those in the control group ( P was 0.039, 0.008, 0.039, respectively). The QSV of aSNr, aSNc, and pSNc in the 6-10 years subgroup were higher than those in the 0-5 years subgroup ( P was <0.001, 0.020, 0.015, respectively). The QSV of the aSNc, pSNc in >10 years subgroup were lower than those in the 6-10 years subgroup ( P=0.037, 0.006). The QSV of aSNr, pSNr in >10 years subgroup were higher than those in the control group ( P was <0.001, 0.001). There were 7 cases of abnormal STS in the 0-5 years subgroup, 11 cases in the 6-10 years subgroup, 12 cases in >10 years subgroup, and 9 cases in the control subgroup, and there was a statistically significant difference in the probability of abnormal STS among the subgroups of the RRMS patients and the control subgroup (χ 2=16.20, P=0.011). Both the scores of STS in the 6-10 years subgroup and >10 years group were positively correlated with the QSV in pSNc ( r s=0.65, P=0.006; r s=0.48, P=0.045). Conclusions:In RRMS patients, SN iron deposition is concentrated on aSNr, pSNr, and aSNc in the 0-5 years subgroup and on aSNr, aSNc and pSNc in the 6-10 years subgroup. The QSVs of all SN subregions have a downward trend in >10 years subgroup compared with that in the 6-10 years subgroup. Both the QSVs of the pSNc in the 6-10 years group and >10 years group are positively related to STS scores. These help explore the potential progression pattern of SN iron deposition in RRMS patients and the cause of abnormal STS in RRMS patients.

Objective:To investigate the effect of using two different input functions to reconstruct 18F-FDG PET/CT Patlak multi-parameter images on the quantitative parameters of lung cancer lesions. Methods:The original whole-body dynamic 18F-FDG PET/CT scan data of lung cancer patients in the Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University were retrospectively analyzed. The total scan time was 75 min. Two input functions were used for Patlak multi-parameter reconstruction: ① Image-derived input function(IDIF)using the Time-activity curve(TAC)of descending aorta from 0 min to 75 min. ② Population-based input function (PBIF) developed by Yale University. Metabolic rate of FDG (MR FDG) and Distribution volume (DV) images were obtained by Patlak multi-parameter analysis software using the above input functions. The region of interest (ROI) method was used to delineate the lesions to obtain multi-parameter quantitative information, including the max, peak and mean value of MR FDG and DV. Paired t-test was used for statistical analysis. Results:The original data of 27 lung cancer patients who received whole-body dynamic 18F-FDG PET/CT imaging were reconstructed by Patlak with two different input functions. The max, peak and mean values of MR FDG-IDIF and MR FDG-PBIF in lung cancer lesions were as follows: (0.26 ± 0.15), (0.19 ± 0.12), (0.14 ± 0.08)μmol·min -1·ml -1 and (0.26 ± 0.15), ( 0.20 ± 0.13), (0.15 ± 0.09)μmol·min -1·ml -1, with no statistically significant difference between two functions( P > 0.05). The max, peak and mean values of DV IDIF and DV PBIF were (165.56 ± 99.89)%, (117.66 ± 72.24)%, (62.16 ± 33.65)% and(170.04 ± 103.93)%, (121.91 ± 73.71)%, (65.05 ± 37.17)%, with no statistically significant difference between two functions ( P > 0.05). Conclusions:The population-based input function may be an alternative for patients who could not lie supine for long time during whole-body dynamic 18F-FDG PET/CT Patlak multi-parameter imaging.

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms

Objective:To explore the safety and feasibility of bilateral axillo-breast approach （BABA） robot in the operation of thyroid cancer in obese women. Methods:The clinical data of 81 obese female patients who underwent da Vinci robotic thyroid cancer surgery（robotic group） at the Department of Thyroid and Breast Surgery, PLA 960 Hospital from May 2018 to December 2021 were retrospectively analyzed and compared with the clinical data of 106 obese female thyroid cancer patients who underwent open surgery（open group） during the same period. The age, body mass index（BMI）, mean time of surgery, mean postoperative drainage, tumor diameter, postoperative tumor stage, number of lymph node dissection in the central and lateral cervical regions, number of positive lymph nodes in the central and lateral cervical regions, postoperative cosmetic outcome satisfaction score, mean postoperative hospital stay and postoperative complications of all patients were counted. The results were analyzed using SPSS 26.0 statistical software, and the count data were compared using the χ² test, and the measurement data were compared using the t test. Results:All patients completed the operation successfully, and there was no conversion in the robot group, postoperative pathological results were all composed of papillary thyroid carcinoma. The operation time in the robot group was（144.62±36.38） min, which was longer than that in the open group（117.06±18.72） min（P<0.05）. The average age of the robot group was（40.25±9.27） years, which was lower than that of the open group（49.59±8.70） years（P<0.05）. The satisfactory score of cosmetic effect in the robot group（9.44±0.65） was higher than that in the open group（5.23±1.07）（P<0.05）. There was no significant difference in tumor diameter, BMI, average postoperative drainage, temporary hypoparathyroidism and recurrent laryngeal nerve injury, number of central and lateral cervical lymph node dissection, number of positive lymph nodes in the central and lateral cervical regions, and average postoperative hospital stay between the two groups. There was no permanent hypoparathyroidism and recurrent laryngeal nerve injury in both groups. Conclusion:The application of BABA pathway robot in thyroid cancer surgery in obese women is safe and feasible, and the cosmetic effect is better after operation.
Humans ; Female ; Adult ; Middle Aged ; Robotics/methods* ; Retrospective Studies ; Recurrent Laryngeal Nerve Injuries ; Thyroidectomy/methods* ; Carcinoma, Papillary/surgery* ; Thyroid Neoplasms/pathology* ; Neck Dissection ; Treatment Outcome