Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Frontiers in Medicine

article

Jiang Zhu¹  Jinxin Zheng²  Longfei Li³  Rui Huang⁴  Haoyu Ren¹  Denghui Wang¹  Zhijun Dai²  Xinliang Su¹ 

[1] Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University;Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University;Department of Health Statistics, School of Public Health, Chongqing Medical University;Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University;Department of General, Ludwig-Maximilians-University

关键词: papillary thyroid carcinoma;    central lymph node metastasis;    machine learning algorithms;    lymph node dissections;    prediction model;   

DOI  :  10.3389/fmed.2021.635771

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY