Consistency and Diagnostic Performance of Coronary Computed Tomography-derived Fractional Flow Reserve: Based on Different Deep Learning Algorithms

doi:10.12114/j.issn.1007-9572.2024.0476

Chinese General Practice ›› 2026, Vol. 29 ›› Issue (24): 3466-3475.DOI: 10.12114/j.issn.1007-9572.2024.0476

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Consistency and Diagnostic Performance of Coronary Computed Tomography-derived Fractional Flow Reserve: Based on Different Deep Learning Algorithms

1. Department of Cardiology, Qilu Hospital of Shandong University/National Key Laboratory of Theory and Innovation of Network Diseases/Key Laboratory of Cardiovascular Remodeling and Function Research of MOE, NHC, CAMS and Shandong Province, Jinan 250012, China
2. Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China

Received:2024-10-18 Revised:2025-10-09 Published:2026-08-20 Online:2026-07-03
Contact: LYU Lijuan, ZHANG Mei

冠状动脉计算机断层扫描衍生的血流储备分数的一致性与诊断性能研究：基于不同深度学习算法

1．250012　山东省济南市，山东大学齐鲁医院心内科　络病理论创新转化全国重点实验室　教育部、国家卫健委、中国医学科学院和山东省心血管重构与功能研究重点实验室
2．250012　山东省济南市，山东大学齐鲁医院放射科

通讯作者: 吕立娟, 张梅
作者简介:
作者贡献：
周以纯提出主要研究目标，负责研究的构思与设计及实施，撰写论文；周以纯、韩业明、张鹏飞、宋雯雯、万晓钰、李奇谋、刘权德、杨威进行数据的收集与整理，统计学处理，图、表的绘制与展示；潘继琛、李昕昊、李笃民、于德新、董梅、梁永锋、胡珊珊、吕立娟、张梅进行论文的修订；吕立娟、张梅负责文章的质量控制与审查，对文章整体负责，监督管理。
基金资助:
国家重点研发项目(2022YFC3602403)

Abstract

Abstract:

Background

Computed tomography-derived fractional flow reserve (CT-FFR) has been shown to have good diagnostic performance for coronary artery stenosis, but the consistency of CT-FFR calculated by different deep learning algorithms has not been evaluated.

Objective

This study aims to assess the consistency of CT-FFR in diagnosing coronary artery stenosis based on two deep learning algorithms and validate its diagnostic performance using invasive coronary angiography (ICA) or invasive fractional flow reserve (FFR) as references.

Methods

From January 2017 to June 2021, a total of 389 patients with suspected or confirmed coronary artery disease (CAD) were enrolled at Qilu Hospital of Shandong University. The cohort included patients who underwent coronary computed tomography angiography (CCTA), ICA, or FFR measurement. Among them, 55 patients underwent ICA within 90 days after CCTA, and 23 patients underwent FFR measurement after CCTA. Bland-Altman analysis was used to evaluate the consistency of CT-FFR based on Software 1 and Software 2, and the diagnostic performance of CT-FFR was compared with that of CCTA, using ICA or invasive FFR as the reference. ROC curves for both CT-FFR methods were plotted in diagnosing coronary artery stenosis, and calculated the area under the curve (AUC), and compared the diagnostic performance of CT-FFR with that of CCTA.

Results

A total of 389 patients were included, comprising 181 men (46.5%) and 208 women (53.5%), with a mean age of 55.1±10.9 years; in total, 1 161 coronary arteries were analyzed. Based on Software 1, 172 vessels (14.8%) were identified as having functionally significant stenosis, while Software 2 identified 114 vessels (9.8%). Bland-Altman plots showed that CT-FFR derived from Software 1 slightly overestimated values, with a mean difference of 0.05 overall (0.05 in LAD, 0.04 in LCX, and 0.05 in RCA). Correlation analysis demonstrated moderate associations between CT-FFR and invasive FFR (r=0.44 for Software 1; r=0.53 for Software 2). Bland-Altman analysis showed good agreement with invasive FFR, with mean differences of -0.03 (Software 1) and -0.06 (Software 2). In diagnostic performance, CCTA had the highest sensitivity (97.8%) and negative predictive value (98.5%), but lower specificity (66.7%) and positive predictive value (57.7%). In contrast, Software 1-based CT-FFR achieved 89.1% sensitivity, 80.8% specificity, 68.3% PPV, 94.1% NPV, and 83.4% accuracy, while Software 2-based CT-FFR showed 80.4% sensitivity, 93.9% specificity, 86.0% PPV, 91.2% NPV, and 89.7% accuracy. ROC curve analysis confirmed that both CT-FFR outperformed CCTA, with AUC values of 0.91 (Software 1), 0.89 (Software 2), compared to 0.82 for CCTA (P<0.05).

Conclusion

Good consistency was observed between the CT-FFR values based on Software 1 and Software 2, although a slight overestimation was found for CT-FFR based on software 1. Overall, CT-FFR demonstrated good diagnostic performance in detecting the functional significance of coronary stenosis.

Key words: Computed tomography, Fractional flow reserve, Computed tomography-derived fractional flow reserve, Deep learning, Coronary artery disease

摘要：

背景

计算机断层扫描衍生的血流储备分数（CT-FFR）已被证明对冠状动脉狭窄具有良好的诊断性能，但不同深度学习算法计算的CT-FFR之间的一致性未被评估。

目的

本研究旨在评估基于两种深度学习算法的CT-FFR诊断冠状动脉狭窄的一致性，并使用介入性冠状动脉造影（ICA）或有创性血流储备分数（FFR）作为参考，验证其诊断性能。

方法

2017年1月—2021年6月，于山东大学齐鲁医院选取389例怀疑或已确诊冠状动脉疾病（CAD）的患者为研究对象，涵盖了接受冠状动脉计算机断层扫描（CCTA）、ICA或FFR测量的患者队列。55例患者在90 d内先后接受了CCTA与ICA检查，其中23例患者接受了CCTA后进行FFR测量。使用Bland-Altman分析评估基于软件1和软件2的CT-FFR的一致性，并以ICA或有创性FFR为参考，绘制两种CT-FFR诊断冠状动脉狭窄的ROC曲线并计算曲线下面积（AUC），比较CT-FFR与CCTA的诊断性能。

结果

本研究共纳入389例患者，其中男181例（46.5%）、女208例（53.5%），平均年龄为（55.1±10.9）岁；共分析1 161条冠状动脉。基于软件1的CT-FFR识别出172条（14.8%）功能学显著狭窄血管，而基于软件2的CT-FFR识别出114条（9.8%）。Bland-Altman分析显示，软件1的CT-FFR整体上有轻微高估，平均差异为0.05，其中左前降支、左回旋支和右冠状动脉的平均差异分别为0.05、0.04和0.05。与有创FFR的比较中，基于软件1和软件2的CT-FFR均表现出中等相关性（r=0.44、0.53），且一致性良好（平均差异分别为-0.03和-0.06）。在诊断性能方面，CCTA的灵敏度（97.8%）和阴性预测值（98.5%）最高，但其特异度（66.7%）和阳性预测值（57.7%）明显低于CT-FFR。基于软件1的CT-FFR灵敏度为89.1%，特异度为80.8%，阳性预测值为68.3%，阴性预测值为94.1%，准确性为83.4%；基于软件2的CT-FFR灵敏度为80.4%，特异度为93.9%，阳性预测值为86.0%，阴性预测值为91.2%，准确性为89.7%。ROC曲线分析显示，两种CT-FFR的诊断价值（AUC=0.91、0.89）均优于CCTA（AUC=0.82，P<0.05）。

结论

基于软件1和软件2的CT-FFR值之间存在良好的一致性，尽管基于软件1的CT-FFR略有高估。总体而言，CT-FFR在检测冠状动脉狭窄的功能性显著性方面表现出良好的诊断性能。

关键词: 计算机断层扫描, 血流储备分数, 计算机断层扫描衍生的血流储备分数, 深度学习, 冠状动脉疾病

CLC Number:

R 541.4

ZHOU Yichun,HAN Yeming,ZHANG Pengfei, et al. Consistency and Diagnostic Performance of Coronary Computed Tomography-derived Fractional Flow Reserve: Based on Different Deep Learning Algorithms[J]. Chinese General Practice, 2026, 29(24): 3466-3475. DOI: 10.12114/j.issn.1007-9572.2024.0476.
周以纯,韩业明,张鹏飞等. 冠状动脉计算机断层扫描衍生的血流储备分数的一致性与诊断性能研究：基于不同深度学习算法[J]. 中国全科医学, 2026, 29(24): 3466-3475. DOI: 10.12114/j.issn.1007-9572.2024.0476.

Figures/Tables 7

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参数	数值
年龄（±s，岁）	55.1±10.9
性别[例（%）]
男	181（46.5）
女	208（53.5）
BMI≥28 kg/m²[例（%）]	84（21.6）
心血管风险因素[例（%）]
吸烟史	111（28.5）
高血压	201（51.7）
糖尿病	70（18.0）
高脂血症	259（66.6）
CAD家族史	58（14.9）
Agaston评分[M（P₂₅，P₇₅），分]	0（0，73.9）
CCTA有效辐射剂量[M（P₂₅，P₇₅），mSv]	7.0（5.7，8.7）
CCTA电压[M（P₂₅，P₇₅），kV]	100（100，100）
冠状动脉主导类型[例（%）]
右主导类型	364（93.6）
平衡型	20（5.1）
左主导类型	5（1.3）
基于软件1的CT-FFR[例（%）]
CT-FFR LAD≤0.80	81（20.8）
CT-FFR LCX≤0.80	36（9.4）
CT-FFR RCA≤0.80	55（14.2）
基于软件2的CT-FFR[例（%）]
CT-FFR LAD≤0.80	68（17.5）
CT-FFR LCX≤0.80	17（4.4）
CT-FFR RCA≤0.80	29（7.5）

参数	数值
年龄（±s，岁）	55.1±10.9
性别[例（%）]
男	181（46.5）
女	208（53.5）
BMI≥28 kg/m²[例（%）]	84（21.6）
心血管风险因素[例（%）]
吸烟史	111（28.5）
高血压	201（51.7）
糖尿病	70（18.0）
高脂血症	259（66.6）
CAD家族史	58（14.9）
Agaston评分[M（P₂₅，P₇₅），分]	0（0，73.9）
CCTA有效辐射剂量[M（P₂₅，P₇₅），mSv]	7.0（5.7，8.7）
CCTA电压[M（P₂₅，P₇₅），kV]	100（100，100）
冠状动脉主导类型[例（%）]
右主导类型	364（93.6）
平衡型	20（5.1）
左主导类型	5（1.3）
基于软件1的CT-FFR[例（%）]
CT-FFR LAD≤0.80	81（20.8）
CT-FFR LCX≤0.80	36（9.4）
CT-FFR RCA≤0.80	55（14.2）
基于软件2的CT-FFR[例（%）]
CT-FFR LAD≤0.80	68（17.5）
CT-FFR LCX≤0.80	17（4.4）
CT-FFR RCA≤0.80	29（7.5）

参数	CCTA狭窄程度≥50.0%	基于软件1的CT-FFR≤0.80	基于软件2的CT-FFR≤0.80
灵敏度	97.8（88.5，100.0）	89.1（76.4，96.4）	80.4（66.1，90.6）
特异度	66.7（56.5，75.8）	80.8（71.7，88.0）	93.9（87.3，97.7）
阳性预测值	57.7（50.7，64.4）	68.3（58.7，76.6）	86.0（73.7，93.1）
阴性预测值	98.5（90.4，99.8）	94.1（87.4，97.4）	91.2（85.2，94.9）
准确性	76.6（68.8，83.2）	83.4（76.4，89.1）	89.7（83.5，94.1）

参数	CCTA狭窄程度≥50.0%	基于软件1的CT-FFR≤0.80	基于软件2的CT-FFR≤0.80
灵敏度	97.8（88.5，100.0）	89.1（76.4，96.4）	80.4（66.1，90.6）
特异度	66.7（56.5，75.8）	80.8（71.7，88.0）	93.9（87.3，97.7）
阳性预测值	57.7（50.7，64.4）	68.3（58.7，76.6）	86.0（73.7，93.1）
阴性预测值	98.5（90.4，99.8）	94.1（87.4，97.4）	91.2（85.2，94.9）
准确性	76.6（68.8，83.2）	83.4（76.4，89.1）	89.7（83.5，94.1）

血管分类	血管数	0.5~<0.6	0.6~<0.7	0.7~<0.8	0.8~<0.9	0.9~1.0
所有冠状动脉
基于软件1的CT-FFR	1 161	38（3.3）	38（3.3）	87（7.5）	166（14.3）	832（71.7）
基于软件2的CT-FFR	1 161	1（0.1）	14（1.2）	79（6.8）	785（67.7）	282（24.3）
χ²值		—	—	64.050	18.123	40.515
P值		0.033	0.074	<0.001	<0.001	<0.001
LAD
基于软件1的CT-FFR	389	15（3.9）	19（4.9）	43（11.1）	71（18.3）	241（62.0）
基于软件2的CT-FFR	389	0	7（1.8）	47（12.1）	299（76.9）	36（9.3）
χ²值			—	14.993	4.002	5.824
P值			0.298	<0.001	0.045	0.016
LCX
基于软件1的CT-FFR	385	4（1.0）	10（2.6）	19（4.9）	48（12.5）	304（79.0）
基于软件2的CT-FFR	385	0	0	12（3.1）	182（47.3）	191（49.6）
χ²值				—	10.148	18.470
P值				<0.001	0.001	<0.001
RCA
基于软件1的CT-FFR	387	19（4.9）	9（2.3）	25（6.5）	47（12.1）	287（74.2）
基于软件2的CT-FFR	387	1（0.3）	7（1.8）	20（5.2）	304（78.6）	55（14.2）
χ²值		—	—	15.450	3.710	4.268
P值		0.049	0.153	<0.001	0.054	0.039

Consistency and Diagnostic Performance of Coronary Computed Tomography-derived Fractional Flow Reserve: Based on Different Deep Learning Algorithms

冠状动脉计算机断层扫描衍生的血流储备分数的一致性与诊断性能研究：基于不同深度学习算法

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