Zengfa Huang, Ruiyao Tang, Xinyu Du, Yi Ding, ZhiWen Yang, Beibei Cao, Mei Li, Xi Wang, Wanpeng Wang, Zuoqin Li, Jianwei Xiao, Xiang Wang
{"title":"基于深度学习的RCA - PCAT和斑块体积对支架植入术患者预后的价值。","authors":"Zengfa Huang, Ruiyao Tang, Xinyu Du, Yi Ding, ZhiWen Yang, Beibei Cao, Mei Li, Xi Wang, Wanpeng Wang, Zuoqin Li, Jianwei Xiao, Xiang Wang","doi":"10.2174/0115734056335065250426150739","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI).</p><p><strong>Methods: </strong>A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality.</p><p><strong>Results: </strong>In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT (p = 0.007) and plaque volume (p = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 2.94, 95%CI: 1.15-7.50, p = 0.025) and plaque volume (HR: 3.91, 95%CI: 1.20-12.75, p = 0.024) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.271).</p><p><strong>Conclusions: </strong>Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CTFFR in patients with PCI.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prognostic Value Of Deep Learning Based RCA PCAT and Plaque Volume Beyond CT-FFR In Patients With Stent Implantation.\",\"authors\":\"Zengfa Huang, Ruiyao Tang, Xinyu Du, Yi Ding, ZhiWen Yang, Beibei Cao, Mei Li, Xi Wang, Wanpeng Wang, Zuoqin Li, Jianwei Xiao, Xiang Wang\",\"doi\":\"10.2174/0115734056335065250426150739\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI).</p><p><strong>Methods: </strong>A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality.</p><p><strong>Results: </strong>In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT (p = 0.007) and plaque volume (p = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 2.94, 95%CI: 1.15-7.50, p = 0.025) and plaque volume (HR: 3.91, 95%CI: 1.20-12.75, p = 0.024) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.271).</p><p><strong>Conclusions: </strong>Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CTFFR in patients with PCI.</p>\",\"PeriodicalId\":54215,\"journal\":{\"name\":\"Current Medical Imaging Reviews\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2025-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Medical Imaging Reviews\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/0115734056335065250426150739\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Medical Imaging Reviews","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0115734056335065250426150739","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Prognostic Value Of Deep Learning Based RCA PCAT and Plaque Volume Beyond CT-FFR In Patients With Stent Implantation.
Aim: The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI).
Methods: A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality.
Results: In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT (p = 0.007) and plaque volume (p = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 2.94, 95%CI: 1.15-7.50, p = 0.025) and plaque volume (HR: 3.91, 95%CI: 1.20-12.75, p = 0.024) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.271).
Conclusions: Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CTFFR in patients with PCI.
期刊介绍：
Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques.
The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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