Prediction Model for Lymph Node Metastasis in Papillary Thyroid Carcinoma Based on Electronic Medical Records

Abstract

Abstract Purpose This study aimed to establish a novel machine learning model for predicting lymph node metastasis(LNM)of patients with papillary thyroid carcinoma (PTC) by utilizing personal electronic medical records (EMR) data. Methods The study included 5076 PTC patients underwent total thyroidectomy or lobectomy with lymph node dissection. Based on the integrated learning approach, this study designed a predictive model for LNM. The predictive model employs deep neural network (DNN) models to identify features within cases and vectorize clinical data from electronic medical records into feature matrices. Subsequently, a classifier based on machine learning algorithms is designed to analyse the feature matrices for prediction LNM in PTC. To mitigate the risk of overfitting commonly associated with machine learning algorithms processing high-dimensional matrices, multiple DNNS are utilized to distribute the overfitting risk. Five mainstream machine learning algorithms (NB, DT, XGB, GBM, RDF) are tested as classifier algorithms in the predictive model. Model performance is assessed using precision, recall, F1, and AUC. Results Among the patients, 2,261 had lymph node metastasis (LNM), with 2,196 displaying central lymph node metastasis (CLNM) and 472 exhibiting lateral cervical lymph node metastasis (LLNM). The RDF model showcased superior predictive performance compared to other models, achieving a testing AUC of 0.98, precision of 0.98, recall of 0.95, and F1 value of 0.97 in predicting LNM. Moreover, it attained an AUC of 0.98, precision of 0.98, recall of 0.94, and an F1 value of 0.96 in predicting CLNM. Regarding the weighting of the feature matrix for various case data types, gender and multi-focus held higher weights, at 1.24 and 1.23 respectively. Conclusion The LNM predictive model proposed in this study could be used as a cost-effective tool for predicting LNM in PTC patients, by utilizing easily available personal electronic medical data, which can provide valuable support to surgeons in devising a personalized treatment plan.