[A clinical study on predicting the surgical difficulty of robot-assisted total thyroidectomy combined with lateral cervical lymph node dissection based on machine learning algorithms]

Objectives: To construct a prediction model based on machine learning algorithms for the surgical difficulty of robot-assisted total thyroidectomy combined with lateral cervical lymph node dissection,and to validate its predictive performance and clinical utility. Methods: This retrospective case series study collected clinical data from 496 patients with differentiated thyroid cancer admitted to the Thyroid and Breast Surgery Department of the 960^th Hospital of the People's Liberation Army Joint Logistics Support Force between January 2014 and July 2024, who underwent robot-assisted total thyroidectomy combined with lateral neck lymph node dissection using the Da Vinci system. There were 143 male cases and 353 female cases,aged (36.5±10.4)years (range: 18 to 70 years). Patients were randomly divided into a training set (n=347) and a validation set (n=149) at a ratio of 7∶3. Clinical characteristics,ultrasound features,and hematological parameters were collected. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression. Based on the selected features,eight machine learning algorithms-including logistic regression,support vector machine (SVM),gradient boosting machine,neural network,random forest,extreme gradient boosting (XGBoost),k-nearest neighbors and adaptive boosting-were utilized to construct prediction models. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC) and F1 score. The Shapley additive explanations (SHAP) method was applied to conduct an interpretable analysis of the optimal model. Results: A total of 8 key features for predicting surgical difficulty were selected through LASSO regression dimensionality reduction: the maximum short-axis diameter of positive lateral cervical lymph nodes,positive lymph nodes in cervical levels Ⅱ and Ⅲ,the number of positive lateral cervical lymph nodes,sex,diffuse thyroid changes on ultrasound,weight,surgeon's years of experience,and thyroid peroxidase antibody (TPOAb) positivity. The SVM model demonstrated the best performance,with the highest AUC (0.815),the highest F1 score (0.708), and a relatively high sensitivity (0.727). SHAP analysis confirmed that lateral cervical lymph node characteristics,higher body weight,male sex,diffuse thyroid changes on ultrasound,and TPOAb positivity were high-risk factors associated with increased surgical difficulty. Conclusions: The model constructed based on patients' preoperative multidimensional features exhibits good predictive performance for the surgical difficulty of Da Vinci robot-assisted total thyroidectomy combined with lateral cervical lymph node dissection. The integration of SHAP interpretable analysis enables an individualized and visualized preoperative risk assessment,which provides a scientific basis for surgeons to rationally allocate medical resources and optimize perioperative management.