Objective To construct machine learning models for predicting short-term outcomes of meningioma patients after surgical resection.Methods The clinical data of 424 meningioma patients who underwent surgical resection at the Department of Neurosurgery,Shanghai Tenth People's Hospital from September 2021 to March 2022 were retrospectively analyzed.The Glasgow outcome scale(GOS)scores of patients were evaluated at discharge,GOS score ≤3 was defined as a poor prognosis.Patients were randomly divided into a training set and a validation set in a 7∶3 ratio.Machine learning models using support vector machines,random forest,gradient boosting,AdaBoost,and multilayer perceptron algorithms were developed on the training set,and their predictive abilities were tested in the validation set.The Shapley Additive Explanations(SHAP)algorithm was used for model interpretation of the better-performing model.Results Total 42 clinical variables and GOS scores were collected from 424 meningioma patients.After selection,23 clinical variables were included in the construction of machine-learning models in the training set.The predictive performance of machine-learning model based on AdaBoost algorithm was the best with an AUC value of 0.925.The SHAP algorithm indicated that in the AdaBoost model,the maximum diameter of the meningioma,blood pressure at admission,preoperative calcium ion concentration,blood urea concentration,and blood creatinine concentration contributed most to decision-making of the model,suggesting a significant correlation between these preoperative clinical features and short-term postoperative prognosis in meningioma patients.Conclusion A machine-learning model based on the AdaBoost algorithm has been constructed in the study,which demonstrates a good performance in predicting short-term adverse outcomes in meningioma patients after surgical resection.
维普
万方数据