Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Meng-Pan Li; Wen-Cai Liu; Bo-Lin Sun; Nan-Shan Zhong; Zhi-Li Liu; Shan-Hu Huang; Zhi-Hong Zhang; Jia-Ming Liu

doi:10.3389/fonc.2022.1054300

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Front Oncol. 2023 Jan 9:12:1054300. doi: 10.3389/fonc.2022.1054300. eCollection 2022.

Authors

Meng-Pan Li^{1

2}, Wen-Cai Liu^{1

2

3}, Bo-Lin Sun^{1

4}, Nan-Shan Zhong^{1

4}, Zhi-Li Liu^{1

4}, Shan-Hu Huang^{1

4}, Zhi-Hong Zhang^{1

4}, Jia-Ming Liu^{1

4}

Affiliations

¹ Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.
² The First Clinical Medical College of Nanchang University, Nanchang, China.
³ Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
⁴ Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China.

Abstract

Objective: The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.

Methods: Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.

Results: A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.

Conclusion: This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.

Keywords: NSCLC; SEER; XGB algorithm; bone metastasis; machine learning.

Grants and funding

This work is supported by the “Double Thousand Plan” Talent Project of Jiangxi Province and the central government guides local funds for scientific and technological development, China (No. 20222ZDH04095).