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人工智能衍生的冠状动脉钙化在F-氟吡哒唑正电子发射断层心肌灌注显像中的增量诊断价值。

Incremental diagnostic value of AI-derived coronary artery calcium in F-flurpiridaz PET Myocardial Perfusion Imaging.

作者信息

Barrett Orit, Shanbhag Aakash, Zaid Ryan, Miller Robert J H, Lemley Mark, Builoff Valerie, Liang Joanna X, Kavanagh Paul B, Buckley Christopher, Dey Damini, Berman Daniel S, Slomka Piotr J

出版信息

medRxiv. 2025 Jul 11:2025.07.07.25330013. doi: 10.1101/2025.07.07.25330013.


DOI:10.1101/2025.07.07.25330013
PMID:40672505
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12265794/
Abstract

BACKGROUND: Positron Emission Tomography (PET) myocardial perfusion imaging (MPI) is a powerful tool for predicting coronary artery disease (CAD). Coronary artery calcium (CAC) provides incremental risk stratification to PET-MPI and enhances diagnostic accuracy. We assessed additive value of CAC score, derived from PET/CT attenuation maps to stress TPD results using the novel 18F-flurpiridaz tracer in detecting significant CAD. METHODS AND RESULTS: Patients from F-flurpiridaz phase III clinical trial who underwent PET/CT MPI with F-flurpiridaz tracer, had available CT attenuation correction (CTAC) scans for CAC scoring, and underwent invasive coronary angiography (ICA) within a 6-month period between 2011 and 2013, were included. Total perfusion deficit (TPD) was quantified automatically, and CAC scores from CTAC scans were assessed using artificial intelligence (AI)-derived segmentation and manual scoring. Obstructive CAD was defined as ≥50% stenosis in Left Main (LM) artery, or 70% or more stenosis in any of the other major epicardial vessels. Prediction performance for CAD was assessed by comparing the area under receiver operating characteristic curve (AUC) for stress TPD alone and in combination with CAC score. Among 498 patients (72% males, median age 63 years) 30.1% had CAD. Incorporating CAC score resulted in a greater AUC: manual scoring (AUC=0.87, 95% Confidence Interval [CI] 0.34-0.90; p=0.015) and AI-based scoring (AUC=0.88, 95%CI 0.85-0.90; p=0.002) compared to stress TPD alone (AUC 0.84, 95% CI 0.80-0.92). CONCLUSIONS: Combining automatically derived TPD and CAC score enhances F-flurpiridaz PET MPI accuracy in detecting significant CAD, offering a method that can be routinely used with PET/CT scanners without additional scanning or technologist time. CONDENSED ABSTRACT: We assessed the added value of CAC score from hybrid PET/CT CTAC scans combined with stress TPD for detecting significant CAD using novel F-flurpiridaz tracer Patients from the F-flurpiridaz phase III clinical trial (n=498, 72% male, median age 63) who underwent PET/CT MPI and ICA within 6-months were included. TPD was quantified automatically, and CAC scores were assessed by AI and manual methods. Adding CAC score to TPD improved AUC for manual (0.87) and AI-based (0.88) scoring versus TPD alone (0.84). Combining TPD and CAC score enhances F-flurpiridaz PET MPI accuracy for CAD detection.

摘要

背景:正电子发射断层扫描(PET)心肌灌注成像(MPI)是预测冠状动脉疾病(CAD)的有力工具。冠状动脉钙化(CAC)为PET-MPI提供了额外的风险分层,并提高了诊断准确性。我们评估了使用新型18F-氟吡哒唑示踪剂从PET/CT衰减图得出的CAC评分对应激总灌注缺损(TPD)结果在检测显著CAD方面的附加价值。 方法与结果:纳入了2011年至2013年期间在6个月内接受了使用18F-氟吡哒唑示踪剂的PET/CT MPI检查、有可用的CT衰减校正(CTAC)扫描用于CAC评分且接受了有创冠状动脉造影(ICA)的18F-氟吡哒唑III期临床试验患者。自动定量总灌注缺损(TPD),并使用人工智能(AI)衍生的分割和手动评分评估CTAC扫描的CAC评分。阻塞性CAD定义为左主干(LM)动脉狭窄≥50%,或任何其他主要心外膜血管狭窄70%或更多。通过比较单独应激TPD以及与CAC评分联合时的受试者操作特征曲线下面积(AUC)来评估CAD的预测性能。在498例患者(72%为男性,中位年龄63岁)中,30.1%患有CAD。纳入CAC评分导致更大的AUC:手动评分(AUC = 0.87,95%置信区间[CI] 0.34 - 0.90;p = 0.015)和基于AI的评分(AUC = 0.88,95%CI 0.85 - 0.90;p = 0.002),相比单独应激TPD(AUC 0.84,95%CI 0.80 - 0.92)。 结论:将自动得出的TPD和CAC评分相结合可提高18F-氟吡哒唑PET MPI检测显著CAD的准确性,提供一种无需额外扫描或技术人员时间即可与PET/CT扫描仪常规使用的方法。 摘要:我们评估了使用新型18F-氟吡哒唑示踪剂,将混合PET/CT CTAC扫描的CAC评分与应激TPD相结合在检测显著CAD方面的附加价值。纳入了18F-氟吡哒唑III期临床试验的患者(n = 498,72%为男性,中位年龄63岁),他们在6个月内接受了PET/CT MPI和ICA检查。自动定量TPD,并通过AI和手动方法评估CAC评分。将CAC评分添加到TPD中可提高手动评分(0.87)和基于AI评分(0.88)的AUC,相比单独TPD(0.84)。将TPD和CAC评分相结合可提高18F-氟吡哒唑PET MPI检测CAD的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e80/12265794/28538e215752/nihpp-2025.07.07.25330013v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e80/12265794/83e05bbb7d8f/nihpp-2025.07.07.25330013v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e80/12265794/423fd9511b98/nihpp-2025.07.07.25330013v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e80/12265794/28538e215752/nihpp-2025.07.07.25330013v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e80/12265794/83e05bbb7d8f/nihpp-2025.07.07.25330013v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e80/12265794/423fd9511b98/nihpp-2025.07.07.25330013v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e80/12265794/28538e215752/nihpp-2025.07.07.25330013v1-f0005.jpg

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本文引用的文献

[1]
Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.

Eur Heart J Cardiovasc Imaging. 2024-6-28

[2]
Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

JACC Cardiovasc Imaging. 2023-5

[3]
Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.

J Nucl Med. 2023-4

[4]
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Eur J Nucl Med Mol Imaging. 2022-10

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J Am Coll Cardiol. 2020-7-28

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J Nucl Cardiol. 2018-11-28

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J Nucl Cardiol. 2016-10

[9]
Combined Quantitative Assessment of Myocardial Perfusion and Coronary Artery Calcium Score by Hybrid 82Rb PET/CT Improves Detection of Coronary Artery Disease.

J Nucl Med. 2015-9

[10]
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JACC Cardiovasc Imaging. 2014-11

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