Merging Multiphase CTA Images and Training Them Simultaneously with a Deep Learning Algorithm Could Improve the Efficacy of AI Models for Lateral Circulation Assessment in Ischemic Stroke

作者全名："Wang, Jingjie; Tan, Duo; Liu, Jiayang; Wu, Jiajing; Huang, Fusen; Xiong, Hua; Luo, Tianyou; Chen, Shanxiong; Li, Yongmei"

作者地址："[Wang, Jingjie; Liu, Jiayang; Luo, Tianyou; Li, Yongmei] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Tan, Duo; Chen, Shanxiong] Southwest Univ, Coll Comp & Informat Sci, Tiansheng Rd, Chongqing 400715, Peoples R China; [Wu, Jiajing] PLA Army, Hosp 958, Dept Radiol, Chongqing 400020, Peoples R China; [Huang, Fusen] Chongqing Med Univ, Affiliated Hosp 1, Dept Anesthesiol, Chongqing 400016, Peoples R China; [Xiong, Hua] Univ Chinese Acad Sci, Chongqing Gen Hosp, Dept Radiol, Chongqing 400013, Peoples R China"

通信作者："Li, YM (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 400016, Peoples R China.; Chen, SX (通讯作者)，Southwest Univ, Coll Comp & Informat Sci, Tiansheng Rd, Chongqing 400715, Peoples R China."

来源：DIAGNOSTICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000833927000001

JCR分区：Q1

影响因子：3.6

年份：2022

卷号：12

期号：7

开始页：　

结束页：　

文献类型：Article

关键词：acute ischemic stroke; collateral circulation; large vessel occlusion; deep learning; 4D-CTA

摘要："We aimed to build a deep learning-based, objective, fast, and accurate collateral circulation assessment model. We included 92 patients who had suffered acute ischemic stroke (AIS) with large vessel occlusion in the anterior circulation in this study, following their admission to our hospital from June 2020 to August 2021. We analyzed their baseline whole-brain four-dimensional computed tomography angiography (4D-CTA)/CT perfusion. The images of the arterial, arteriovenous, venous, and late venous phases were extracted from 4D-CTA according to the perfusion time-density curve. The subtraction images of each phase were created by subtracting the non-contrast CT. Each patient was marked as having good or poor collateral circulation. Based on the ResNet34 classification network, we developed a single-image input and a multi-image input network for binary classification of collateral circulation. The training and test sets included 65 and 27 patients, respectively, and Monte Carlo cross-validation was employed for five iterations. The network performance was evaluated based on its precision, accuracy, recall, F-1-score, and AUC. All the five performance indicators of the single-image input model were higher than those of the other model. The single-image input processing network, combining multiphase CTA images, can better classify AIS collateral circulation. This automated collateral assessment tool could help to streamline clinical workflows, and screen patients for reperfusion therapy."

基金机构："Medical Research Program of the Chongqing National Health Commission; Chongqing Science and Technology Bureau, China [2021MSXM155]"

基金资助正文："This research was funded by the Medical Research Program of the Chongqing National Health Commission and Chongqing Science and Technology Bureau, China (grant number 2021MSXM155)."