Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis

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dc.authorid0000-0003-0563-5964
dc.authorid0000-0002-2776-6650
dc.authorid0000-0001-7664-5786
dc.authorwosidOzmen, Vahit/AAE-3904-2020
dc.contributor.authorKizildag Yirgin, İnci
dc.contributor.authorKoyluoglu, Yılmaz Onat
dc.contributor.authorSeker, Mustafa Ege
dc.contributor.authorGürdal, Sibel Özkan
dc.contributor.authorÖzaydın, Ayşe Nilüfer
dc.contributor.authorÖzçınar, Beyza
dc.contributor.authorArıbal, Erkin
dc.date.accessioned2022-05-11T14:35:00Z
dc.date.available2022-05-11T14:35:00Z
dc.date.issued2022
dc.departmentFakülteler, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü, Genel Cerrahi Ana Bilim Dalı
dc.description.abstractPurpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists' assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95% CI = 0.801-0.905) and the cut-off value for risk score was 34.5% with a sensitivity of 72.8% and a specificity of 88.3% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3% for radiologists, 72.7% for AI, and 83.6% for radiologist and AI team-up. AI detected 72.7% of all cancers on its own, of which 77.5% were screen-detected, 15% were interval cancers, and 7.5% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.
dc.identifier.doi10.1177/15330338221075172
dc.identifier.issn1533-0346
dc.identifier.issn1533-0338
dc.identifier.pmid35060413
dc.identifier.scopus2-s2.0-85123655254
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1177/15330338221075172
dc.identifier.urihttps://hdl.handle.net/20.500.11776/8169
dc.identifier.volume21
dc.identifier.wosWOS:000747838500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorGürdal, Sibel Özkan
dc.language.isoen
dc.publisherSage Publications Inc
dc.relation.ispartofTechnology in Cancer Research & Treatment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectartificial intelligence
dc.subjectbreast cancer
dc.subjectdeep learning
dc.subjectmammography
dc.subjectscreening
dc.subjectComputer-Aided Detection
dc.subjectBreast-Cancer
dc.subjectArtificial-Intelligence
dc.subjectMortality
dc.subjectCarcinoma
dc.subjectSurveillance
dc.titleDiagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis
dc.typeArticle

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