Development of a Machine Learning-Based Predictive Model for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.
Central lymph nodes metastasis (CLNM) is common in papillary thyroid. Microcarcinoma (PTMC). Whilst prophylactic central lymph node dissection (CLND) can prevent further CLNM, it remains controversial. An accurate model to predict CLNM is therefore necessary for patients with PTMC.
This study incorporated 228 patients with general clinical information, thyroid related serological examination and ultrasound of CLNM prediction, divided into training and validation sets randomly at 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for key features screening. Eight machine learning models were developed, evaluated by cross-validation and performance comparison (the area under curve, calibration curve and decision curve analysis). Shapley Additive exPlanations (SHAP) value analysis provided the interpretability of the model.
Age, gender, tumor diameter, T3, T4, TPOAb and ultrasound of CLNM prediction were identified as key features of CLNM in patients. Support Vector Machine (SVM) model with 0.783 accuracy and 0.805 specificity in validation set was considered as the most favorable performance. Age, gender and tumor diameter were the top three contributing variables in SVM model.
This study established a machine learning-based framework for predicting CLNM in PTMC, with the SVM model demonstrating superior stability and clinical utility among the evaluated algorithms. While these results are preliminary, they provide a promising tool to assist in tailoring prophylactic CLND strategies, potentially reducing unnecessary surgical intervention.
This study incorporated 228 patients with general clinical information, thyroid related serological examination and ultrasound of CLNM prediction, divided into training and validation sets randomly at 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for key features screening. Eight machine learning models were developed, evaluated by cross-validation and performance comparison (the area under curve, calibration curve and decision curve analysis). Shapley Additive exPlanations (SHAP) value analysis provided the interpretability of the model.
Age, gender, tumor diameter, T3, T4, TPOAb and ultrasound of CLNM prediction were identified as key features of CLNM in patients. Support Vector Machine (SVM) model with 0.783 accuracy and 0.805 specificity in validation set was considered as the most favorable performance. Age, gender and tumor diameter were the top three contributing variables in SVM model.
This study established a machine learning-based framework for predicting CLNM in PTMC, with the SVM model demonstrating superior stability and clinical utility among the evaluated algorithms. While these results are preliminary, they provide a promising tool to assist in tailoring prophylactic CLND strategies, potentially reducing unnecessary surgical intervention.