Machine Learning-Based Prediction of Lymph Node Metastasis Among Osteosarcoma Patients

Wenle Li; Yafeng Liu; Wencai Liu; Zhi-Ri Tang; Shengtao Dong; Wanying Li; Kai Zhang; Chan Xu; Zhaohui Hu; Haosheng Wang; Zhi Lei; Qiang Liu; Chunxue Guo; Chengliang Yin

doi:10.3389/fonc.2022.797103

Machine Learning-Based Prediction of Lymph Node Metastasis Among Osteosarcoma Patients

Front Oncol. 2022 Apr 20:12:797103. doi: 10.3389/fonc.2022.797103. eCollection 2022.

Authors

Wenle Li^{1

2}, Yafeng Liu^{3

4}, Wencai Liu⁵, Zhi-Ri Tang⁶, Shengtao Dong⁷, Wanying Li², Kai Zhang^{1

2}, Chan Xu², Zhaohui Hu⁸, Haosheng Wang⁹, Zhi Lei¹⁰, Qiang Liu¹, Chunxue Guo¹¹, Chengliang Yin¹²

Affiliations

¹ Department of Orthopedics, Xianyang Central Hospital, Xianyang, China.
² Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.
³ School of Medicine, Anhui University of Science and Technology, Huainan, China.
⁴ Affiliated Cancer Hospital, Anhui University of Science and Technology, Huainan, China.
⁵ Department of Orthopaedic Surgery, the First Affiliated Hospital of Nanchang University, Nanchang, China.
⁶ School of Physics and Technology, Wuhan University, Wuhan, China.
⁷ Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China.
⁸ Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China.
⁹ Department of Orthopaedics, The Second Hospital of Jilin University, Changchun, China.
¹⁰ Chronic Disease Division, Luzhou Center for Dcontrol and Prevention, Luzhou, China.
¹¹ Biostatistics Department, Hengpu Yinuo (Beijing) Technology Co., Ltd, Beijing, China.
¹² Faculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.

Abstract

Background: Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms.

Methods: A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals.

Results: Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals.

Conclusions: T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.

Keywords: SEER; lymph node metastasis; machine learning algorithm; multicenter; osteosarcoma; web calculator.