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

Abstract

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 months. Multivariate Cox regression analysis identified mild or moderate dysplasia as an independent risk factor for recurrence. The EST model performs the best in predicting postoperative recurrence of IP, with C-index of 0.968 and 0.878 in the training and testing sets. SHAP emphasizes five important predictive factors for recurrence, including bone defects, orbital involvement, smoking, no processing of tumor attachment sites and drinking. Conclusions To our knowledge, this is the first study to use multiple ML models to predict postoperative recurrence of IP. The EST model has the best predictive performance, with SHAP emphasizing several key predictive factors for IP recurrence. This study emphasizes the practicality of machine learning algorithms in predicting IP clinical outcomes, providing valuable insights into the potential for improving clinical decision-making.

Keywords

• Inverted papilloma
• Machine learning
• Prediction model
• Recurrence