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| Establishment of machine learning model for predicting bone metastasis in patients with lung adenosquamous carcinoma |
| ZHU Yinghao 1, 2, WANG Shiqi 3, ZHANG Wei 1, 2, LIU Yu 1, 2. |
| 1.Department of Cardiothoracic Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325035, China; 2.The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou 325035, China; 3.Department of Internal Medicine, Niansanli Park of the Forth Affiliated Hospital of Zhejiang University, Jinhua 322000, China |
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| Cite this article: |
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ZHU Yinghao,WANG Shiqi,ZHANG Wei, et al. Establishment of machine learning model for predicting bone metastasis in patients with lung adenosquamous carcinoma[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2023, 53(7): 588-594，封三.
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Abstract Objective: To explore the value of machine learning algorithm in the prediction model of lung adeno-squamous cell carcinoma bone metastasis. Methods: Data of patients with lung adeno-squamous cell carcinoma were obtained from the Surveillance, Epidemiology and End Results Database (SEER) and the First Affiliated Hospital of Wenzhou Medical University between January 2017 and December 2021. In order to build the prediction model, six algorithms including random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Multi-Layer Perceptron (MLP) and KNearest Neighbor (kNN) were applied. ROC curve was used to evaluate the prediction capability of these models. Results: Medical records of 1,919 eligible patients were obtained from the SEER database, and 51 obtained from the First Affiliated Hospital of Wenzhou Medical University. Machine learning model results showed that lung adenosquamous metastasis to organs other than bone and lymph node metastasis were the most important predictors of bone metastasis. The attributes of machine learning model based on XGBoost algorithm were more accurate and efficient. Conclusion: Bone metastasis of lung adenosquamous cell carcinoma leads to poor prognosis. The predictive model based on machine learning can accurately predict the possibility of bone metastasis in patients with lung adenosquamous cell at early stage, which is of great significance in clinical decision-making.
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