Evidence map›Paper›PMID 42192315›Full record

ArticleBMC medical imaging2026

Comparison of systolic and diastolic CT-FFR for myocardial ischemia diagnosis.

Weifeng Guo, Aizhu Sheng, Yuan Wang, Yige Lu, Shihai Zhao, Lekang Yin, Yingjie Zhao, Haijia Xu, Hanbo Zhang, Guanyu Qiao and 10 more

Abstract readComparative Study
In one paragraph

Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

20 authors.

Weifeng Guo *Department Radiology, Shanghai Geriatric Medical Center, 2560 Chunshen Road, Shanghai, 200237, China.
Aizhu Sheng *Department of Radiology, Ningbo No.2 Hospital, Ningbo, 315000, China.
Yuan Wang *Shanghai Municipal Institute for Cardiovascular Disease, Shanghai, 200032, China.
Yige LuShanghai Municipal Institute for Cardiovascular Disease, Shanghai, 200032, China.
Shihai ZhaoDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Lekang YinDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Yingjie ZhaoDepartment of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Haijia XuShanghai Municipal Institute for Cardiovascular Disease, Shanghai, 200032, China.
Hanbo ZhangShanghai Municipal Institute for Cardiovascular Disease, Shanghai, 200032, China.
Guanyu QiaoShanghai Municipal Institute for Cardiovascular Disease, Shanghai, 200032, China.
Li ShenDepartment of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Yang PangDepartment of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Jiasheng YinDepartment of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Zhifeng YaoDepartment of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Chenguang LiDepartment of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Shan YangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Cheng YanDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Wei He *Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. 16301050198@fudan.edu.cn.
Furong He *Shanghai Medical College, Fudan University, Shanghai, 200032, China. 19301050310@fudan.edu.cn.
Mengsu Zeng *Department Radiology, Shanghai Geriatric Medical Center, 2560 Chunshen Road, Shanghai, 200237, China. zeng_mengsu@163.com.

Funding

Joint Research Development Project between Shenkang and United Imaging on Clinical Research and Translation SKLY2022CRT201Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region 2022D01C652
6 · The paper itself

Abstract

backgroundCT-derived fractional flow reserve (CT-FFR) is a powerful tool for identifying hemodynamic ischemia. Coronary CT angiography (CCTA) images with the best quality in a cardiac cycle are conventionally reconstructed in clinical practice. To compare the diagnostic performance of machine learning (ML)-based CT-FFR between systolic and diastolic phases in identifying myocardial ischemia, using invasive fractional flow reserve (FFR) as a reference standard.

methodsFrom December 2020 to October 2021, consecutive coronary artery disease (CAD) patients who underwent coronary computed tomography angiography (CCTA) and invasive FFR were prospectively enrolled. Prospective electrocardiographic (ECG)-triggered scan from 30% to 80% of R-R interval was applied on image extraction of all the patients. CT-FFR was implemented in systolic and diastolic phases for each target vessel.

resultsNinety-six patients (mean age 62 ± 8, 31.3% female) with 98 vessels were successfully simulated in both systolic and diastolic phases. The area under the curve (AUC) was significantly higher in diastolic CT-FFR (0.885 vs. 0.790, p < 0.001). The limits of agreement (LoA) range was narrower in diastolic CT-FFR (-0.134 to 0.148), compared with systolic CT-FFR (-0.234 to 0.273). Diastolic CT-FFR performed better than systolic CT-FFR in sensitivity (81.5% vs. 66.7%), specificity (83.1% vs. 71.8%), and accuracy (82.7% vs. 70.4%, all p < 0.05). The LoA range was narrower for diastolic CT-FFR across all stenosis degree groups, with diagnostic performance better in sensitivity (84.0% vs. 68.0%) and accuracy (81.4% vs. 58.1%, p < 0.001) among severe stenosis group.

conclusionsIn ML-based CT-FFR methods, diastolic CT-FFR showed better diagnostic performance and narrower LoA than systolic CT-FFR.

trial registrationThe study has been registered through the Ethics Committee of Zhongshan Hospital, Fudan University, with the clinical trial number B2020-088R. The registration date is May 15, 2020.

Indexed as

Computed Tomography AngiographyCoronary AngiographyFractional Flow Reserve, MyocardialMyocardial IschemiaAgedCoronary Artery DiseaseDiastoleFemaleHumansMachine LearningMaleMiddle AgedProspective StudiesSensitivity and SpecificitySystoleCoronary artery diseaseCoronary computed tomography angiographyMyocardial ischemia

Identifiers

PMID42192315
PMCPMC13386928

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.