ArticlePloS one2026
Rationale and design of a multicenter, prospective, diagnostic clinical study: A study protocol for evaluating the diagnostic validation of deep learning-based noninvasive CT-FFR for in-stent restenosis.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCurrently, there are many diagnostic strategies for in-stent restenosis (ISR) used clinically, including invasive coronary angiography (ICA), coronary computed tomography angiography (CCTA), and Fractional Flow Reserve (FFR). CCTA is not recommended for post-stent implantation patients owing to suboptimal image quality caused by artifacts. The FFR application is limited by its procedural complexity. Precise evaluation may be achieved by using computed tomography-derived fractional flow reserve (CT-FFR), which combines computational fluid dynamics (CFD) with CCTA. Anatomical and functional assessments of ISR lesions could be integrated effectively as well in this way. However, the computational complexity and prolonged processing time may hinder its utility in clinical use.
methodsThis study is a multicenter, prospective, diagnostic study, aiming to establish a deep learning-based CT-FFR model for the accurate assessment of ISR and to validate its diagnostic performance using invasive FFR as the reference standard. This study will be carried out in Beijing Anzhen Hospital and 6 subcenters in China. We planned to prospectively enroll 331 post-stent implantation patients with available CCTA data since June 2022, and invasive FFR will be performed within 3 months when clinically indicated. Patient recruitment is currently ongoing. Among them, 250 patients from Beijing Anzhen Hospital will be used to adapt and extend the existing DEEPVESSEL model, a deep learning-based CT-FFR computational software designed for the non-invasive functional assessment of coronary artery disease and previously validated in de novo coronary lesions, for application in the assessment of in-stent restenosis (ISR), and 81 patients from the other 6 subcenters will be used in external validation. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value with their corresponding 95% confidence intervals (CIs) were calculated for CT-FFR. The receiver operating characteristic (ROC) curve was analyzed, and the area under the curve (AUC) was calculated. The McNemar test and Bland-Altman plot will be used to examine the diagnostic consistency between CT-FFR and invasive FFR. The correlation was analyzed by Spearman's correlation coefficient. TRIAL REGISTRATION NUMBER: ChiCTR2200058822.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.