肿瘤科
医学
节的
内科学
转移
基底细胞
列线图
机器学习
人工智能
癌症
作者
Min Seob Kwak,Young Gyu Eun,Jung-Woo Lee,Young Chan Lee
出处
期刊:Head and Neck-journal for The Sciences and Specialties of The Head and Neck
日期:2021-03-31
卷期号:43 (8): 2316-2324
摘要
BACKGROUND This study aimed to develop and compare machine learning (ML) based predictive models for lymph node metastasis (LNM) in early T classification oral squamous cell carcinoma (OSCC). METHODS We used data from the Surveillance Epidemiology and End Results Database to develop and validate the predictive models for LNM in patients with T1, T2 OSCC. Using simple clinical and histopathological data, we developed six ML algorithms to predict LNM. The predictive performance of models was compared. RESULTS The areas under the receiver operating characteristic curves (AUCs) of the six models ranged from 0.768 to 0.956. The best prediction performance was achieved with a XGBoost (AUC = 0.956). Permutation importance analysis showed that tumor size is the most important feature in predicting metastasis. CONCLUSIONS We developed a simplified and reproducible ML-based predictive model for metastasis in early T classification OSCC that could be helpful for the decision of a treatment strategy.
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