{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T22:16:40Z","timestamp":1793398600164,"version":"4.5.0"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"46","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Dutch Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011949","name":"Abbott Vascular","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011949","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016238","name":"Dutch Ministry of Economic Affairs and Climate Policy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100016238","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000046","name":"Abbott","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000046","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016036","name":"Health\u223cHolland","doi-asserted-by":"publisher","award":["LSHM19102"],"award-info":[{"award-number":["LSHM19102"]}],"id":[{"id":"10.13039\/100016036","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Coronary thin-cap fibroatheromas (TCFA) are associated with adverse outcome, but identification of TCFA requires expertise and is highly time-demanding. This study evaluated the utility of artificial intelligence (AI) for TCFA identification in relation to clinical outcome.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The PECTUS-AI study is a secondary analysis from the prospective observational PECTUS-obs study, in which 438 patients with myocardial infarction underwent optical coherence tomography (OCT) of all fractional flow reserve-negative non-culprit lesions (i.e. target lesions). OCT images were analyzed for the presence of TCFA by an independent core laboratory (CL-TCFA) and OCT-AID, a recently developed and validated AI segmentation algorithm (AI-TCFA). The primary outcome was defined as the composite of death from any cause, non-fatal myocardial infarction or unplanned revascularisation at 2 years (\u00b130 days), excluding procedural and stent-related events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 414 patients, AI-TCFA and CL-TCFA were identified in 143 (34.5%) and 124 (30.0%) patients, respectively. AI-TCFA within the target lesion was significantly associated with the primary outcome [hazard ratio (HR) 1.99, 95% confidence interval (CI) 1.02\u20133.90, P = .04], while the HR for CL-TCFA was non-significant (1.67, 95% CI: .84\u20133.30, P = .14). When evaluating the complete pullback, AI-TCFA showed an even stronger association with the primary outcome (HR 5.50, 95% CI: 1.94\u201315.62, P &amp;lt; .001; negative predictive value 97.6%, 95% CI: 94.0%\u201399.3%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI-based OCT image analysis allows standardized identification of patients at increased risk of adverse cardiovascular outcome, offering an alternative to manual image analysis. Furthermore, AI-assisted evaluation of complete imaged segments results in better prognostic discrimatory value than evaluation of the target lesion only.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eurheartj\/ehaf595","type":"journal-article","created":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T11:43:04Z","timestamp":1754134984000},"page":"5032-5041","source":"Crossref","is-referenced-by-count":20,"title":["Artificial intelligence-based identification of thin-cap fibroatheromas and clinical outcomes: the PECTUS-AI study"],"prefix":"10.1093","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7471-1006","authenticated-orcid":false,"given":"Rick H J A","family":"Volleberg","sequence":"first","affiliation":[{"name":"Department of Cardiology, Radboud University Medical Center , PO Box 9101, Nijmegen 6500 HB ,","place":["the 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Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5191-6180","authenticated-orcid":false,"given":"Pierandrea","family":"Cancian","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Physics, Amsterdam University Medical Center , Amsterdam ,","place":["the Netherlands"]},{"name":"Quantitative Healthcare Analysis Group, Informatics Institute, University of Amsterdam , Amsterdam ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4998-6363","authenticated-orcid":false,"given":"Joske L","family":"van der Zande","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Radboud University Medical Center , PO Box 9101, Nijmegen 6500 HB ,","place":["the Netherlands"]},{"name":"Diagnostic Image Analysis Group, Radboud University Medical Center , Nijmegen ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7455-049X","authenticated-orcid":false,"given":"Xiaojin","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Physics, Amsterdam University Medical Center , Amsterdam ,","place":["the Netherlands"]},{"name":"Quantitative Healthcare Analysis Group, Informatics Institute, University of Amsterdam , Amsterdam ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8609-1550","authenticated-orcid":false,"given":"Jan-Quinten","family":"Mol","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Radboud University Medical Center , PO Box 9101, Nijmegen 6500 HB ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1370-7369","authenticated-orcid":false,"given":"Tomasz","family":"Roleder","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Wroc\u0142aw University of Science and Technology , Wroc\u0142aw ,","place":["Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8157-8055","authenticated-orcid":false,"given":"Mathias","family":"Prokop","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Radboud University Medical Center , Nijmegen ,","place":["the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clara I","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Physics, Amsterdam University Medical Center , Amsterdam ,","place":["the Netherlands"]},{"name":"Quantitative Healthcare Analysis Group, Informatics 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