使用光子计数 CT 和人工智能在经导管主动脉瓣置换术检查过程中评估冠状动脉疾病。

IF 4.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging Pub Date : 2024-07-01 DOI:10.1016/j.diii.2024.01.010

Jan M. Brendel , Jonathan Walterspiel , Florian Hagen , Jens Kübler , Jean-François Paul , Konstantin Nikolaou , Meinrad Gawaz , Simon Greulich , Patrick Krumm , Moritz Winkelmann

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引用次数: 0

摘要

目的：本研究旨在评估光子计数（PC）CT 联合人工智能冠状动脉计算机断层扫描（PC-CCTA）狭窄量化和分数血流储备预测（FFRai）在经导管主动脉瓣置换术（TAVR）检查中评估冠状动脉疾病（CAD）的能力：这项回顾性三级单中心研究纳入了2021年10月至2023年6月期间转诊进行TAVR术前检查的连续重症主动脉瓣狭窄患者。所有患者均在三个月内接受了 PC-CCTA 和 ICA 诊断，以作为参考标准。PC-CCTA 狭窄量化（50% 水平）和 FFRai（0.8 水平）通过两个深度学习模型（CorEx、Spimed-AI）进行预测。结果：共评估了 260 名患者（138 名男性，122 名女性），平均年龄为 78.7 ± 8.1（标准差）岁（年龄范围：51-93 岁）。126/260例患者（48.5%）的ICA显示存在明显的CAD。每位患者的敏感性、特异性、阳性预测值、阴性预测值和诊断准确性分别为 96.0%（95% 置信区间 [CI]：91.0-98.7）、68.7%（95% 置信区间：60.1-76.4）、74.3%（95% 置信区间：69.1-78.8）、94.8%（95% 置信区间：60.1-76.4）、74.3%（95% 置信区间：69.1-78.8）、74.3%（95% 置信区间：69.1-78.8FFRai的曲线下面积分别为96.8%（95% CI：92.1-99.1）、87.3%（95% CI：80.5-92.4）、87.8%（95% CI：82.2-91.8）、96.7%（95% CI：91.7-98.7）和91.9%（95% CI：87.9-94.9）。FFRai 的曲线下面积为 0.92（95% CI：0.88-0.95），而 PC-CCTA 为 0.82（95% CI：0.77-0.87）（P < 0.001）。在FFRai指导下，260名患者中有121名（46.5%）无需进行ICA，而仅使用PC-CCTA的260名患者中有97名（37.3%）无需进行ICA（P < 0.001）：结论：基于深度学习的光子计数 FFRai 评估提高了 PC-CCTA ≥ 50% 狭窄检测的准确性，减少了 ICA 的需求，可纳入临床 TAVR 工作检查以评估 CAD。

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Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence

Purpose

The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up.

Materials and methods

Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed.

Results

A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51–93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0–98.7), 68.7% (95% CI: 60.1–76.4), 74.3 % (95% CI: 69.1–78.8), 94.8% (95% CI: 88.5–97.8), and 81.9% (95% CI: 76.7–86.4) for PC-CCTA, and 96.8% (95% CI: 92.1–99.1), 87.3% (95% CI: 80.5–92.4), 87.8% (95% CI: 82.2–91.8), 96.7% (95% CI: 91.7–98.7), and 91.9% (95% CI: 87.9–94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88–0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77–0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001).

Conclusion

Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

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来源期刊

Diagnostic and Interventional Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

8.50

自引率

29.10%

发文量

126

审稿时长

11 days

期刊介绍： Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English. Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.