Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics

Abstract

Different machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients with intracranial aneurysms (192 ruptured and 384 unruptured intracranial aneurysms) from two institutions are included and randomly divided into training and validation cohorts in a ratio of 7:3. Of the 107 radiomics features extracted from computed tomography angiography images, seven features stood out. Then, radiomics features and 12 common machine learning algorithms, including the decision-making tree, support vector machine, logistic regression, Gaussian Naive Bayes, k-nearest neighbor, random forest, extreme gradient boosting, bagging classifier, AdaBoost, gradient boosting, light gradient boosting machine, and CatBoost were applied to construct models for predicting ruptured intracranial aneurysms, and the predictive performance of all models was compared. In the validation cohort, the area under curve (AUC) values of models based on AdaBoost, gradient boosting, and CatBoost for predicting ruptured intracranial aneurysms were 0.889, 0.883, and 0.864, respectively, with no significant differences among them. Of note, the performance of these models was significantly superior to that of the other nine models. The AUC of the AdaBoost model in the cross-validation was within the range of 0.842 to 0.918. Radiomics models based on the machine learning algorithms can be used to predict ruptured intracranial aneurysms, and the prediction efficacy differs among machine learning algorithms. The boosting algorithms might be superior in the application of radiomics combined with the machine learning algorithm to predict aneurysm ruptures.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

“…However, a study by Ludwig et al ( 33 ) found that radiomics-derived morphological features extracted from DSA did not significantly enhance the predictive performance for aneurysm rupture, contradicting Liu’s assertion that flatness is the most critical morphological determinant for predicting aneurysm stability. Yang et al ( 34 ) utilized radiomics features to distinguish between ruptured and unruptured intracranial aneurysms in the middle cerebral artery, constructing classification models with 12 common machine learning algorithms. The models built on AdaBoost, XGBoost, and CatBoost algorithms outperformed others, with AUCs of 0.889, 0.883, and 0.864, respectively.…”

Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research

“…CatBoost uses a sequential boosting method that considers the order of categorical variables during the boosting process. In addition, it uses symmetric decision trees to improve generalization performance and includes various techniques to optimize sequencing in the learning process [22,23].…”