{"version":"6.9","hitCount":1,"request":{"queryString":"EXT_ID:40473527 AND SRC:MED","internalQuery":"EXT_ID:40473527 AND SRC:MED","resultType":"CORE","cursorMark":"*","pageSize":25,"sort":"","synonym":false},"resultList":{"result":[{"id":"40473527","source":"MED","pmid":"40473527","doi":"10.1016/j.clbc.2025.05.007","title":"Improved Breast Cancer Detection with Artificial Intelligence in a Real-World Digital Breast Tomosynthesis Screening Program.","authorString":"Nepute JA, Peratikos M, Toledano AY, Salvas JP, Delks H, Shisler JL, Hoffmeister JW, Madden CM.","authorList":{"author":[{"fullName":"Nepute JA","lastName":"Nepute","firstName":"Joshua A","initials":"JA","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University Health - West Central Region, Avon, IN."}]}},{"fullName":"Peratikos M","lastName":"Peratikos","firstName":"Meridith","initials":"M","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Biostatistics Consulting, LLC, Kensington, MD."}]}},{"fullName":"Toledano AY","lastName":"Toledano","firstName":"Alicia Y","initials":"AY","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Biostatistics Consulting, LLC, Kensington, MD."}]}},{"fullName":"Salvas JP","lastName":"Salvas","firstName":"John P","initials":"JP","authorId":{"type":"ORCID","value":"0000-0003-1550-8758"},"authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Indiana University School of Medicine, Indianapolis, IN."}]}},{"fullName":"Delks H","lastName":"Delks","firstName":"Haley","initials":"H","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University Health - West Central Region, Avon, IN."}]}},{"fullName":"Shisler JL","lastName":"Shisler","firstName":"Julie L","initials":"JL","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"JLS Consulting, Jupiter, FL."}]}},{"fullName":"Hoffmeister JW","lastName":"Hoffmeister","firstName":"Jeffrey W","initials":"JW","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"iCAD, Inc, Nashua, NH."}]}},{"fullName":"Madden CM","lastName":"Madden","firstName":"Colleen M","initials":"CM","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IUH Ball Memorial Hospital, Muncie, IN. Electronic address: comadden@iu.edu."}]}}]},"authorIdList":{"authorId":[{"type":"ORCID","value":"0000-0003-1550-8758"}]},"dataLinksTagsList":{"dataLinkstag":["altmetrics"]},"journalInfo":{"issue":"8","volume":"25","journalIssueId":4054011,"dateOfPublication":"2025 Dec","monthOfPublication":12,"yearOfPublication":2025,"journal":{"medlineAbbreviation":"Clin Breast Cancer","title":"Clinical breast cancer","essn":"1938-0666","issn":"1526-8209","isoabbreviation":"Clin Breast Cancer","nlmid":"100898731"},"printPublicationDate":"2025-12-01"},"pubYear":"2025","pageInfo":"808-816.e5","abstractText":"<h4>Objective</h4>The purpose of this study is to compare radiologists' breast cancer screening performance before and after the implementation of an artificial intelligence (AI) detection system for digital breast tomosynthesis (DBT).<h4>Materials and methods</h4>This retrospective study included 4 radiologists reading DBT screening mammograms across 3 clinical sites during 2 distinct time periods. The pre-AI time period from September 1, 2018 to August 31, 2019 included 10,322 standard DBT interpretations with a computer-aided detection system. The post-AI from January 1 to March 18, 2020 and May 4 to December 31, 2020 included 6,407 DBT interpretations with concurrent use of a deep learning AI support system. Endpoints included cancer detection rate (CDR), abnormal interpretation rate (AIR), and positive predictive values for cancer among screenings with abnormal interpretation (PPV1) and biopsies performed (PPV3). Estimates and 95% confidence intervals (CIs) for each radiologist were calculated for each time point and the difference across time periods.<h4>Results</h4>The CDR per 1000 exams increased from 3.7 without AI to 6.1 with AI (difference 2.4, P = .008, 95% CI: 0.6, 4.2). The AIR was 8.2% without AI and 6.5% with AI (difference -1.7, P < .001, 95% CI: -2.5, -0.8). The PPV1 increased from 4.2% to 8.8% with AI implementation (difference 4.6, P < .001, 95% CI: 3.0, 6.3) and PPV3 increased from 32.3% to 56.5% with AI support (difference 24.2, P = .033, 95% CI: 2.0, 46.4).<h4>Conclusion</h4>Real-world interpretation of DBT after implementation of an AI detection system resulted in increased CDR, reduced AIR, and significantly increased PPV1 and PPV3.","affiliation":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University Health - West Central Region, Avon, IN.","publicationStatus":"ppublish","language":"eng","pubModel":"Print-Electronic","pubTypeList":{"pubType":["Journal Article"]},"meshHeadingList":{"meshHeading":[{"majorTopic_YN":"N","descriptorName":"Breast","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"DG","qualifierName":"diagnostic imaging"},{"majorTopic_YN":"N","abbreviation":"PA","qualifierName":"pathology"}]}},{"majorTopic_YN":"N","descriptorName":"Humans"},{"majorTopic_YN":"Y","descriptorName":"Breast Neoplasms","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"DG","qualifierName":"diagnostic imaging"},{"majorTopic_YN":"N","abbreviation":"DI","qualifierName":"diagnosis"}]}},{"majorTopic_YN":"Y","descriptorName":"Radiographic Image Interpretation, Computer-Assisted","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"MT","qualifierName":"methods"}]}},{"majorTopic_YN":"Y","descriptorName":"Mammography","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"MT","qualifierName":"methods"},{"majorTopic_YN":"N","abbreviation":"SN","qualifierName":"statistics & numerical data"}]}},{"majorTopic_YN":"N","descriptorName":"Retrospective Studies"},{"majorTopic_YN":"Y","descriptorName":"Artificial Intelligence"},{"majorTopic_YN":"N","descriptorName":"Aged"},{"majorTopic_YN":"N","descriptorName":"Middle Aged"},{"majorTopic_YN":"N","descriptorName":"Female"},{"majorTopic_YN":"Y","descriptorName":"Early Detection of Cancer","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"MT","qualifierName":"methods"},{"majorTopic_YN":"N","abbreviation":"SN","qualifierName":"statistics & numerical data"}]}},{"majorTopic_YN":"N","descriptorName":"Deep Learning"}]},"keywordList":{"keyword":["Breast Cancer Screening","mammography","Breast Imaging Clinical Operations","Breast Imaging Quality And Outcomes"]},"subsetList":{"subset":[{"code":"IM","name":"Index Medicus"}]},"fullTextUrlList":{"fullTextUrl":[{"availability":"Subscription required","availabilityCode":"S","documentStyle":"doi","site":"DOI","url":"https://doi.org/10.1016/j.clbc.2025.05.007"}]},"isOpenAccess":"N","inEPMC":"N","inPMC":"N","citedByCount":4,"hasReferences":"Y","hasTextMinedTerms":"Y","hasDbCrossReferences":"N","hasPDF":"N","hasBook":"N","hasSuppl":"N","hasLabsLinks":"Y","hasData":"N","license":"cc by-nc-nd","authMan":"N","epmcAuthMan":"N","nihAuthMan":"N","hasTMAccessionNumbers":"N","dateOfCompletion":"2025-11-23","dateOfCreation":"2025-06-05","dateOfRevision":"2025-11-23","electronicPublicationDate":"2025-05-09","firstPublicationDate":"2025-05-09","firstIndexDate":"2025-06-11","hasEvaluations":"N"}]}}