Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models.

ABSTRACT:

Objectives

Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment.

Methods

This study collected 210 patients underwent IP resection surgery at a university hospital from January 2010 to December 2023. Six machine learning algorithms including ExtraSurvivalTrees (EST), GradientBoostingSurvivalAnalysis (GBSA), RandomSurvivalForest (RSF), SurvivalSVM, Coxnet and Coxph, were used to construct the prediction models. Shapley Additive Explanations (SHAP) values were used to explain the importance of various features in predicting IP recurrence.

Results

We found that the recurrence rate of IP patients is 20.00%, with a median recurrence time of 35.5 m

SUBMITTER: Miao S 

PROVIDER: S-EPMC11533278 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature