Automatic differentiation of ruptured and unruptured intracranial aneurysms on computed tomography angiography based on deep learning and radiomics

作者全名："Feng, Junbang; Zeng, Rong; Geng, Yayuan; Chen, Qiang; Zheng, Qingqing; Yu, Fei; Deng, Tie; Lv, Lei; Li, Chang; Xue, Bo; Li, Chuanming"

作者地址："[Feng, Junbang; Yu, Fei; Deng, Tie; Lv, Lei; Li, Chang; Xue, Bo; Li, Chuanming] Chongqing Univ Cent Hosp, Med Imaging Dept, 1 Jiankang Rd, Chongqing 400014, Peoples R China; [Feng, Junbang; Yu, Fei; Deng, Tie; Li, Chang; Xue, Bo; Li, Chuanming] Chongqing Emergency Med Ctr, Med Imaging Dept, 1 Jiankang Rd, Chongqing 400014, Peoples R China; [Zeng, Rong; Zheng, Qingqing] Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, 74 Linjiang Rd, Chongqing 400010, Peoples R China; [Geng, Yayuan; Chen, Qiang] Shukun Beijing Network Technol Co Ltd, Dept Res & Dev, Room 801,Jinhui Bldg,Qiyang Rd, Beijing 200232, Peoples R China"

通信作者："Xue, B; Li, CM (通讯作者)，Chongqing Univ Cent Hosp, Med Imaging Dept, 1 Jiankang Rd, Chongqing 400014, Peoples R China.; Xue, B; Li, CM (通讯作者)，Chongqing Emergency Med Ctr, Med Imaging Dept, 1 Jiankang Rd, Chongqing 400014, Peoples R China."

来源：INSIGHTS INTO IMAGING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000982166300002

JCR分区：Q1

影响因子：4.1

年份：2023

卷号：14

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Computed tomography angiography; Intracranial aneurysm; Rupture; Deep learning; Radiomics

摘要："Objectives Rupture of intracranial aneurysm is very dangerous, often leading to death and disability. In this study, deep learning and radiomics techniques were used to automatically detect and differentiate ruptured and unruptured intracranial aneurysms.Materials and methods 363 ruptured aneurysms and 535 unruptured aneurysms from Hospital 1 were included in the training set. 63 ruptured aneurysms and 190 unruptured aneurysms from Hospital 2 were used for independent external testing. Aneurysm detection, segmentation and morphological features extraction were automatically performed with a 3-dimensional convolutional neural network (CNN). Radiomic features were additionally computed via pyradiomics package. After dimensionality reduction, three classification models including support vector machines (SVM), random forests (RF), and multi-layer perceptron (MLP) were established and evaluated via area under the curve (AUC) of receiver operating characteristics. Delong tests were used for the comparison of different models.Results The 3-dimensional CNN automatically detected, segmented aneurysms and calculated 21 morphological features for each aneurysm. The pyradiomics provided 14 radiomics features. After dimensionality reduction, 13 features were found associated with aneurysm rupture. The AUCs of SVM, RF and MLP on the training dataset and external testing dataset were 0.86, 0.85, 0.90 and 0.85, 0.88, 0.86, respectively, for the discrimination of ruptured and unruptured intracranial aneurysms. Delong tests showed that there was no significant difference among the three models.Conclusions In this study, three classification models were established to distinguish ruptured and unruptured aneurysms accurately. The aneurysms segmentation and morphological measurements were performed automatically, which greatly improved the clinical efficiency."

基金机构：Science-Health Joint Medical Scientific Research Project of Chongqing [2022QNXM013]

基金资助正文：This study has received funding from the Science-Health Joint Medical Scientific Research Project of Chongqing (2022QNXM013).