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

Hongyu Wang,

Zhiqiang He, Jiang Xu

и другие.

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Янв. 29, 2025

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

Язык: Английский

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

Hongyu Wang,

Zhiqiang He, Jiang Xu

и другие.

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Янв. 29, 2025

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

Язык: Английский

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