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Publications

Journal Article Epublish

PubMed: 40018413 DOI: 10.3389/fonc.2025.1525414

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

Front Oncol 15:1525414

Authors: Hongyu Wang, Zhiqiang He, Jiayang Xu, Ting Chen, Jingtian Huang, Lihong Chen, Xin Yue

Affiliations

  • Otolaryngology, Head and Neck Surgery Department, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. Otolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, China. Otolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
  • Otolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, China. Otolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.

Abstract

Background

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 early-stage SGLC is limited. This study seeks to create and validate a predictive model through the application of machine learning (ML) algorithms.

Methods

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.

Results

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 applicability.

Conclusions

Our study indicates that combining ML algorithms with clinical data can effectively predict LNM in patients diagnosed with early-stage SGLC. This is the first study to apply ML models in predicting LNM in early-stage SGLC patients.

MeSH

Big Data, Lymph Nodes, Laryngeal Neoplasms, Neoplasms, Algorithms, Machine Learning, Neoplasm Metastasis, Lymph, Precision Medicine, Learning