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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1525414
This article is part of the Research Topic Harnessing Big Data for Precision Medicine: Revolutionizing Diagnosis and Treatment Strategies View all 17 articles

Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer

Provisionally accepted
Hongyu Wang Hongyu Wang 1Zhiqiang He Zhiqiang He 2*Jiayang Xu Jiayang Xu 1*Ting Chen Ting Chen 2*Jingtian Huang Jingtian Huang 1*Lihong Chen Lihong Chen 1*Xin Yue Xin Yue 1
  • 1 Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China
  • 2 Department of Otorhinolaryngology Head and Neck Surgery, Fujian Provincial Hospital, Fuzhou, Fujian Province, China

The final, formatted version of the article will be published soon.

    Cervical lymph node metastasis (LNM) is a significant factor that leads to a poor prognosis in laryngeal cancer. Early-stage supraglottic laryngeal cancer (SGLC) is prone to LNM. However, research on risk factors for predicting cervical LNM in earlystage SGLC is limited. This study seeks to create and validate a predictive model through the application of machine learning (ML) algorithms.The training set and internal validation set data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data from 78 early-stage SGLC patients were collected from Fujian Provincial Hospital for independent external validation. We identified four variables associated with cervical LNM and developed six ML models based on these variables to predict LNM in early-stage SGLC patients.In the two cohorts, 167 (47.44%) and 26 (33.33%) patients experienced LNM, respectively. Age, T stage, grade, and tumor size were identified as independent predictors of LNM. All six ML models performed well, and in both internal and independent external validations, the eXtreme Gradient Boosting (XGB) model outperformed the other models, with AUC values of 0.87 and 0.80, respectively. The decision curve analysis demonstrated that the ML models have excellent clinical

    Keywords: big data, precision medicine, early-stage supraglottic laryngeal cancer, lymph node metastasis, machine learning

    Received: 09 Nov 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Wang, He, Xu, Chen, Huang, Chen and Yue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Zhiqiang He, Department of Otorhinolaryngology Head and Neck Surgery, Fujian Provincial Hospital, Fuzhou, 350001, Fujian Province, China
    Jiayang Xu, Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China
    Ting Chen, Department of Otorhinolaryngology Head and Neck Surgery, Fujian Provincial Hospital, Fuzhou, 350001, Fujian Province, China
    Jingtian Huang, Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China
    Lihong Chen, Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China

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