Building Distant Metastasis Models for HNSCC Using Machine Learning Techniques

Background : Accurate assessment of the risk of distant metastasis of head and neck squamous cell carcinoma (HNSCC) is important for the development of personalized treatment and prognosis. The purpose of this study was to explore the risk factors for distant metastasis of HNSCC and to establish a predictive model using machine learning methods. Materials and methods: We designed a retrospective cross-sectional study with a cohort from the SEER database (affiliated with the National Cancer Institute). A total of 31,060 cases of head and neck cancer were included by our inclusion criteria. We constructed four machine learning models—Logistic Regression, Decision Tree, XGBoost, and Neural Network—to predict the risk of distant metastasis in HNSCC patients and compared the performance of the four models. Accuracy, precision, recall, and F1-score were used to evaluate the performance of the model. The evaluation ability and clinical practicability of the model were verified by comparing the area under the curve and the receiver operating characteristic curve. Results : The receiver operating characteristic of the four models ranged from 0.681 to 0.847. The average accuracy of all algorithms was 77 %, and XGBoost had the highest accuracy of 85.119 %. Among the four models, XGBoost and Logistic Regression had the highest precision, both with precision greater than 79. Neural Network had the highest recall and F1-score. Decision Tree had the lowest accuracy and recall. Among the four models, the area under the curve of Decision Tree was the lowest, at 0.690, whereas that of XGBoost was the highest, at 0.846. Overall, XGBoost had the best predictive effect. Conclusion : XGBoost had the highest classification accuracy, so this machine learning method could be used to predict distant metastasis of HNSCC. The application of machine learning algorithms can stratify patients with HNSCC in clinic, which is conducive to the development of personalized treatment plans. Clinical Relevance :The findings of this study have significant implications clinical management of HMSCC.The superior predictive performance of XGBoost,as demonstrated by its high precision and area under the Decision Curve,suggests that this machine learning algorithm could be effectively integrated into clinical practice to predict distant metastasis in HMSCC patients.This has the potential to enhance the accuracy of prognostic assessments, thereby facilitating more informed treatment planning and personalized care.