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

作者全名："Yang, Beisheng; Li, Wenjie; Wu, Xiaojia; Zhong, Weijia; Wang, Jing; Zhou, Yu; Huang, Tianxing; Zhou, Lu; Zhou, Zhiming"

作者地址："[Yang, Beisheng; Li, Wenjie; Wu, Xiaojia; Zhong, Weijia; Wang, Jing; Zhou, Yu; Huang, Tianxing; Zhou, Lu; Zhou, Zhiming] Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing 400000, Peoples R China; [Zhou, Lu] Chongqing Med Univ, Affiliated Hosp 3, Dept Radiol, Chongqing 400000, Peoples R China"

通信作者："Zhou, ZM (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing 400000, Peoples R China."

来源：DIAGNOSTICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001055822600001

JCR分区：Q1

影响因子：3

年份：2023

卷号：13

期号：16

开始页：　

结束页：　

文献类型：Article

关键词：intracranial aneurysm; rupture; radiomics; machine learning; computed tomography angiography

摘要："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."

基金机构："Natural Science Foundation of Chongqing, China [CSTB2022NSCQ-MSX0116]"

基金资助正文："This study was sponsored by the Natural Science Foundation of Chongqing, China (Grant No. CSTB2022NSCQ-MSX0116)."