Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography

作者全名：Zeng, Lu; Zhao, Xiao-Yan; Wen, Li; Jing, Yang; Xu, Jing-Xu; Huang, Chen-Cui; Zhang, Dong; Wang, Guang-Xian

作者地址：[Zeng, Lu; Wang, Guang-Xian] Chongqing Med Univ, Banan Hosp, Dept Radiol, 659 Yunan St, Chongqing 401320, Peoples R China; [Wen, Li; Zhang, Dong] Army Med Univ, Xinqiao Hosp, Dept Radiol, Affiliated Hosp 2, Chongqing, Peoples R China; [Jing, Yang] Huiying Med Technol Beijing, Beijing, Peoples R China; [Xu, Jing-Xu; Huang, Chen-Cui] Beijing Deepwise & League PHD Technol Co Ltd, Dept Res Collaborat, R&D Ctr, Beijing, Peoples R China

通信作者：Wang, GX (通讯作者)，Chongqing Med Univ, Banan Hosp, Dept Radiol, 659 Yunan St, Chongqing 401320, Peoples R China.

来源：QUANTITATIVE IMAGING IN MEDICINE AND SURGERY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001223121600020

JCR分区：Q2

影响因子：2.9

年份：2024

卷号：14

期号：4

开始页：　

结束页：　

文献类型：Article

关键词：Intracranial aneurysms (IAs); computed tomography angiography (CTA); deep learning; convolutional neural network (CNN); stability

摘要：Background: It is crucial to distinguish unstable from stable intracranial aneurysms (IAs) as early as possible to derive optimal clinical decision-making for further treatment or follow-up. The aim of this study was to investigate the value of a deep learning model (DLM) in identifying unstable IAs from computed tomography angiography (CTA) images and to compare its discriminatory ability with that of a conventional Methods: From August 2011 to May 2021, a total of 1,049 patients with 681 unstable IAs and 556 stable IAs were retrospectively analyzed. IAs were randomly divided into training (64%), internal validation (16%), and test sets (20%). Convolutional neural network (CNN) analysis and conventional logistic regression (LR) were used to predict which IAs were unstable. The area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the discriminating ability of the models. One hundred and ninety-seven patients with 229 IAs from Banan Hospital were used for external validation sets. Results: The conventional LRM showed 11 unstable risk factors, including clinical and IA characteristics. The LRM had an AUC of 0.963 [95% confidence interval (CI): 0.941-0.986], a sensitivity, specificity and accuracy on the external validation set of 0.922, 0.906, and 0.913, respectively, in predicting unstable IAs. In predicting unstable IAs, the DLM had an AUC of 0.771 (95% CI: 0.582-0.960), a sensitivity, specificity and accuracy on the external validation set of 0.694, 0.929, and 0.782, respectively. Conclusions: The CNN-based DLM applied to CTA images did not outperform the conventional LRM in predicting unstable IAs. The patient clinical and IA morphological parameters remain critical factors for ensuring IA stability. Further studies are needed to enhance the diagnostic accuracy.

基金机构：Science and Technology Commission of Chongqing City, China [CSTB2023NSCQ-MSX0668]; Joint Project of Science and Health of Chongqing City, China [2023MSXM022]; Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN202200407]

基金资助正文：<B>Acknowledgments</B> Funding: This study was supported by the Science and Technology Commission of Chongqing City, China (No. CSTB2023NSCQ-MSX0668) , the Joint Project of Science and Health of Chongqing City, China (No. 2023MSXM022) and the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN202200407) .