Abstract

Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms.Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance.Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/).Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC.

Highlights

  • Papillary thyroid carcinoma (PTC) is one of the most common type of endocrine malignancies with a favorable prognosis [1, 2]

  • We developed six types of ML algorithms to model our data: Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGBoost), Random forest (RF), Decision tree (DT), and Neural network (NNET)

  • The results showed that male gender, larger tumor size, multifocality, Delphian lymph node (DLN) metastasis, and tumor located in inferior pole [vs. upper pole,] are independent positive predictors of central lymph node metastasis (CLNM) while older age was a negative predictor

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Summary

Introduction

Papillary thyroid carcinoma (PTC) is one of the most common type of endocrine malignancies with a favorable prognosis [1, 2]. The clinical community has reached a general consensus that central lymph node dissection (CLND) for therapeutic purposes is appropriate in PTC patients with suspected cervical lymph node metastasis (LNM) [5]. PCLND is not recommended for a subset of patients with small (T1 or T2), non-invasive, clinically node-negative (cN0) PTC according to the 2015 American Thyroid Association (ATA) guidelines [9], whereas the Japanese Society of Thyroid Surgery and the Chinese Thyroid Association both strongly recommend routine pCLND for cN0 PTC patients in order to stage disease and prevent recurrence. Ideal treatment decisionmaking should be based upon individual patients rather than “one size fits all” approach recommended by guidelines. This highlights the importance of accurate prediction of CLNM occurrence with a more personalized therapeutical schedule

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