{"doi":"10.1007/s00330-024-10650-6","title":"Comparison of machine learning–based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease","abstract":null,"journal":"European Radiology","year":2024,"id":608699,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1563639,"name":"Shihai Zhao","orcid":null,"position":1,"is_corresponding":false},{"id":1563640,"name":"Haijia Xu","orcid":null,"position":2,"is_corresponding":false},{"id":829941,"name":"Wei He","orcid":"0000-0001-5801-9044","position":3,"is_corresponding":false},{"id":1563641,"name":"Lekang Yin","orcid":null,"position":4,"is_corresponding":false},{"id":1563642,"name":"Zhifeng Yao","orcid":null,"position":5,"is_corresponding":false},{"id":1563643,"name":"Zhihan Xu","orcid":null,"position":6,"is_corresponding":false},{"id":734155,"name":"Hang Jin","orcid":"0000-0002-6810-2527","position":7,"is_corresponding":false},{"id":848102,"name":"Dong Wu","orcid":"0000-0002-2842-1144","position":8,"is_corresponding":false},{"id":675058,"name":"Chenguang Li","orcid":"0000-0003-2884-6414","position":9,"is_corresponding":false},{"id":1158316,"name":"Shan Yang","orcid":"0000-0003-0583-3637","position":10,"is_corresponding":false},{"id":734156,"name":"Mengsu Zeng","orcid":"0000-0001-6054-0824","position":11,"is_corresponding":false},{"id":1563638,"name":"Weifeng Guo","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Comparison of machine learning–based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease","abstract":"OBJECTIVES: To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis.\nMETHODS: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference.\nRESULTS: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01).\nCONCLUSION: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis.\nCLINICAL RELEVANCE STATEMENT: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other.\nKEY POINTS: • Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.","is_dataset_classified":null,"base_score":2.302585092994046,"endowment":2.302585092994046,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38409549","pmcid":null,"openalex_id":"https://openalex.org/W4392153243","authors":[],"funders":[{"funder_name":"Shanghai Municipal Key Clinical Specialty","grant_id":"shslczdzk03202","title":null}],"total_grants":1,"fwci":2.3862,"citation_percentile":0.88372545,"influential_citations":0,"citation_trend":[{"year":2024,"count":2},{"year":2025,"count":4},{"year":2026,"count":3}],"oa_status":"closed","license":"https://www.springernature.com/gp/researchers/text-and-data-mining","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/s00330-024-10650-6.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1007/s00330-024-10650-6/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1007/s00330-024-10650-6","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38409549","host_type":"repository"}],"fields_of_study":["Cardiac Imaging and Diagnostics","Coronary Interventions and Diagnostics","MRI in cancer diagnosis","Humans","Male","Female","Middle Aged","Fractional Flow Reserve, Myocardial","Machine Learning","Coronary Artery Disease","Prospective Studies","Magnetic Resonance Imaging","Coronary Angiography","Coronary Stenosis","Aged","Computed Tomography Angiography","Myocardial Perfusion Imaging","Tomography, X-Ray Computed"],"mesh_terms":["Machine Learning","Computed Tomography Angiography","Aged","Coronary Artery Disease","Female","Humans","Magnetic Resonance Imaging","Male","Middle Aged","Prospective Studies","Tomography, X-Ray Computed","Coronary Angiography","Coronary Stenosis","Fractional Flow Reserve, Myocardial","Myocardial Perfusion Imaging"],"keywords":["Fractional flow reserve","Medicine","Coronary artery disease","Neuroradiology","Perfusion","Radiology","Interventional radiology","Stenosis","Perfusion scanning","Cardiac magnetic resonance","Cardiology","Magnetic resonance imaging","Ischemia","Ultrasound","Myocardial perfusion imaging","Internal medicine","Myocardial infarction","Coronary angiography","Neurology","Fractional flow reserve (myocardial)","Multidetector computed tomography"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T20:26:59.900686Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}