Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection.

IF 1.7 2区 医学 Q3 PERIPHERAL VASCULAR DISEASE Journal of Endovascular Therapy Pub Date : 2024-10-01 Epub Date: 2023-03-16 DOI:10.1177/15266028231160101
Min Zhou, Xiaoyuan Luo, Xia Wang, Tianchen Xie, Yonggang Wang, Zhenyu Shi, Manning Wang, Weiguo Fu
{"title":"Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection.","authors":"Min Zhou, Xiaoyuan Luo, Xia Wang, Tianchen Xie, Yonggang Wang, Zhenyu Shi, Manning Wang, Weiguo Fu","doi":"10.1177/15266028231160101","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiography (CTA).</p><p><strong>Methods: </strong>A total of 147 patients with acute or subacute TBAD who underwent proximal TEVAR at a single center were retrospectively reviewed. The boundary of aorta was manually segmented, and the point clouds of each aorta were obtained. Prediction of negative aortic remodeling or reintervention was accomplished by a convolutional neural network (CNN) and a point cloud neural network (PC-NN), respectively. The discriminatory value of the established models was mainly evaluated by the area under the receiver operating characteristic curve (AUC) in the test set.</p><p><strong>Results: </strong>The mean follow-up time was 34.0 months (range: 12-108 months). During follow-up, a total of 25 (17.0%) patients were identified as having negative aortic remodeling, and 16 (10.9%) patients received reintervention. The AUC (0.876) by PC-NN for predicting negative aortic remodeling was superior to that obtained by CNN (0.612, p=0.034) and similar to the AUC by PC-NN combined with clinical features (0.884, p=0.92). As to reintervention, the AUC by PC-NN was significantly higher than that by CNN (0.805 vs 0.579; p=0.042), and AUCs by PC-NN combined with clinical features and PC-NN alone were comparable (0.836 vs 0.805; p=0.81).</p><p><strong>Conclusion: </strong>The CTA-based deep learning algorithms may assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.</p><p><strong>Clinical impact: </strong>Negative aortic remodeling is the leading cause of late reintervention after proximal thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection (TBAD), and possesses great challenge to endovascular repair. Early recognizing high-risk patients is of supreme importance for optimizing the follow-up interval and therapy strategy. Currently, clinicians predict the prognosis of these patients based on several imaging signs, which is subjective. The computed tomography angiography-based deep learning algorithms may incorporate abundant morphological information of aorta, provide with a definite and objective output value, and finally assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.</p>","PeriodicalId":50210,"journal":{"name":"Journal of Endovascular Therapy","volume":" ","pages":"910-918"},"PeriodicalIF":1.7000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Endovascular Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15266028231160101","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/16 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose: This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiography (CTA).

Methods: A total of 147 patients with acute or subacute TBAD who underwent proximal TEVAR at a single center were retrospectively reviewed. The boundary of aorta was manually segmented, and the point clouds of each aorta were obtained. Prediction of negative aortic remodeling or reintervention was accomplished by a convolutional neural network (CNN) and a point cloud neural network (PC-NN), respectively. The discriminatory value of the established models was mainly evaluated by the area under the receiver operating characteristic curve (AUC) in the test set.

Results: The mean follow-up time was 34.0 months (range: 12-108 months). During follow-up, a total of 25 (17.0%) patients were identified as having negative aortic remodeling, and 16 (10.9%) patients received reintervention. The AUC (0.876) by PC-NN for predicting negative aortic remodeling was superior to that obtained by CNN (0.612, p=0.034) and similar to the AUC by PC-NN combined with clinical features (0.884, p=0.92). As to reintervention, the AUC by PC-NN was significantly higher than that by CNN (0.805 vs 0.579; p=0.042), and AUCs by PC-NN combined with clinical features and PC-NN alone were comparable (0.836 vs 0.805; p=0.81).

Conclusion: The CTA-based deep learning algorithms may assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.

Clinical impact: Negative aortic remodeling is the leading cause of late reintervention after proximal thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection (TBAD), and possesses great challenge to endovascular repair. Early recognizing high-risk patients is of supreme importance for optimizing the follow-up interval and therapy strategy. Currently, clinicians predict the prognosis of these patients based on several imaging signs, which is subjective. The computed tomography angiography-based deep learning algorithms may incorporate abundant morphological information of aorta, provide with a definite and objective output value, and finally assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
斯坦福 B 型主动脉夹层胸腔内血管主动脉修复术后远端主动脉重塑的深度学习预测。
目的:本研究旨在开发一种深度学习模型,利用计算机断层血管造影术(CTA)预测斯坦福B型主动脉夹层(TBAD)患者近端胸腔内血管主动脉修复术(TEVAR)后的远端主动脉重塑情况:方法:对在一个中心接受近端 TEVAR 的 147 例急性或亚急性 TBAD 患者进行了回顾性研究。人工分割主动脉边界并获得每条主动脉的点云。通过卷积神经网络(CNN)和点云神经网络(PC-NN)分别对主动脉负重塑或再介入进行预测。建立的模型的判别价值主要通过测试集的接收者操作特征曲线下面积(AUC)进行评估:平均随访时间为 34.0 个月(范围:12-108 个月)。在随访期间,共有 25 例(17.0%)患者被确定为主动脉重塑不良,16 例(10.9%)患者接受了再介入治疗。PC-NN 预测负性主动脉重塑的 AUC(0.876)优于 CNN(0.612,P=0.034),与 PC-NN 结合临床特征的 AUC(0.884,P=0.92)相似。至于再干预,PC-NN的AUC明显高于CNN(0.805 vs 0.579;p=0.042),PC-NN结合临床特征的AUC与PC-NN单独的AUC相当(0.836 vs 0.805;p=0.81):结论:基于CTA的深度学习算法可帮助临床医生自动预测急性或亚急性TBAD TEVAR术后远端主动脉重塑的情况:主动脉负重塑是斯坦福B型主动脉夹层(TBAD)近端胸腔内血管主动脉修复术(TEVAR)后晚期再介入的主要原因,对血管内修复术构成巨大挑战。早期识别高危患者对于优化随访间隔和治疗策略至关重要。目前,临床医生根据一些影像学征象来预测这些患者的预后,但这是主观的。基于计算机断层扫描血管造影的深度学习算法可以结合丰富的主动脉形态学信息,提供明确客观的输出值,最终帮助临床医生自动预测急性或亚急性 TBAD TEVAR 术后远端主动脉重塑的情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.30
自引率
15.40%
发文量
203
审稿时长
6-12 weeks
期刊介绍: The Journal of Endovascular Therapy (formerly the Journal of Endovascular Surgery) was established in 1994 as a forum for all physicians, scientists, and allied healthcare professionals who are engaged or interested in peripheral endovascular techniques and technology. An official publication of the International Society of Endovascular Specialists (ISEVS), the Journal of Endovascular Therapy publishes peer-reviewed articles of interest to clinicians and researchers in the field of peripheral endovascular interventions.
期刊最新文献
The Value of Different Systemic Inflammatory Response Indicators in the Long-term Prognosis of Type B Aortic Dissection Patients Undergoing Thoracic Endovascular Aortic Repair. Effect of Statins on the Prognosis After Thoracic Endovascular Aortic Repair for Patients With Acute Type B Aortic Dissection. Potential of D-Dimer as a Tool to Rule Out Sac Expansion in Patients With Persistent Type 2 Endoleaks After Endovascular Aneurysm Repair. Long-Term Outcomes and Late Complications of Thoracic Endovascular Aortic Repair Using the GORE TAG or Conformable GORE TAG. Long-term Outcomes of the AcoArt II-BTK Trial: Drug-Coated Balloon Angioplasty Compared With Uncoated Balloons for the Treatment of Infrapopliteal Artery Lesions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1