Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer
BackgroundCervical 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...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1525414/full |
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| author | Hongyu Wang Hongyu Wang Hongyu Wang Zhiqiang He Zhiqiang He Zhiqiang He Jiayang Xu Jiayang Xu Ting Chen Ting Chen Ting Chen Jingtian Huang Jingtian Huang Jingtian Huang Lihong Chen Lihong Chen Lihong Chen Xin Yue Xin Yue Xin Yue |
| author_facet | Hongyu Wang Hongyu Wang Hongyu Wang Zhiqiang He Zhiqiang He Zhiqiang He Jiayang Xu Jiayang Xu Ting Chen Ting Chen Ting Chen Jingtian Huang Jingtian Huang Jingtian Huang Lihong Chen Lihong Chen Lihong Chen Xin Yue Xin Yue Xin Yue |
| author_sort | Hongyu Wang |
| collection | DOAJ |
| description | BackgroundCervical 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.MethodsThe 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.ResultsIn 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.ConclusionsOur 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. |
| format | Article |
| id | doaj-art-ff2474c16ef44559bb4ecb2d760f4b72 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-ff2474c16ef44559bb4ecb2d760f4b722025-02-04T13:07:38ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011510.3389/fonc.2025.15254141525414Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancerHongyu Wang0Hongyu Wang1Hongyu Wang2Zhiqiang He3Zhiqiang He4Zhiqiang He5Jiayang Xu6Jiayang Xu7Ting Chen8Ting Chen9Ting Chen10Jingtian Huang11Jingtian Huang12Jingtian Huang13Lihong Chen14Lihong Chen15Lihong Chen16Xin Yue17Xin Yue18Xin Yue19Otolaryngology, Head and Neck Surgery Department, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fujian Provincial Hospital, Fuzhou, ChinaOtolaryngology, Head and Neck Surgery Department, Fuzhou University Affiliated Provincial Hospital, Fuzhou, ChinaBackgroundCervical 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.MethodsThe 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.ResultsIn 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.ConclusionsOur 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.https://www.frontiersin.org/articles/10.3389/fonc.2025.1525414/fullbig dataprecision medicineearly-stage supraglottic laryngeal cancerlymph node metastasismachine learning |
| spellingShingle | Hongyu Wang Hongyu Wang Hongyu Wang Zhiqiang He Zhiqiang He Zhiqiang He Jiayang Xu Jiayang Xu Ting Chen Ting Chen Ting Chen Jingtian Huang Jingtian Huang Jingtian Huang Lihong Chen Lihong Chen Lihong Chen Xin Yue Xin Yue Xin Yue Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer Frontiers in Oncology big data precision medicine early-stage supraglottic laryngeal cancer lymph node metastasis machine learning |
| title | Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer |
| title_full | Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer |
| title_fullStr | Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer |
| title_full_unstemmed | Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer |
| title_short | Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer |
| title_sort | development and validation of a machine learning model to predict the risk of lymph node metastasis in early stage supraglottic laryngeal cancer |
| topic | big data precision medicine early-stage supraglottic laryngeal cancer lymph node metastasis machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1525414/full |
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