Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study

Basit Öğe Kaydı
dc.authorscopusid58059638100
dc.authorscopusid15045330900
dc.authorscopusid58161836700
dc.authorscopusid6508373389
dc.authorscopusid6602384879
dc.authorscopusid6503979172
dc.authorscopusid6701681647
dc.contributor.authorAslan, Ö.
dc.contributor.authorOktay, A.
dc.contributor.authorKatuk, B.
dc.contributor.authorErdur, R.C.
dc.contributor.authorDikenelli, O.
dc.contributor.authorYeniay, L.
dc.contributor.authorZekioğlu, O.
dc.date.accessioned2024-08-25T18:45:26Z
dc.date.available2024-08-25T18:45:26Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractPURPOSE High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy. METHODS A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed. RESULTS Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was ob-served between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-under-the-curve score were obtained in classifying the data as benign or malignant. CONCLUSION Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum exci-sions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology–pathology compatibility and by using an artificial intelligence model. © 2023, Turkish Society of Radiology. All rights reserved.en_US
dc.identifier.doi10.5152/dir.2022.211047
dc.identifier.endpage267en_US
dc.identifier.issn1305-3825
dc.identifier.issue2en_US
dc.identifier.pmid36987868en_US
dc.identifier.scopus2-s2.0-85151112160en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage260en_US
dc.identifier.trdizinid1168764en_US
dc.identifier.urihttps://doi.org/10.5152/dir.2022.211047
dc.identifier.urihttps://hdl.handle.net/11454/101562
dc.identifier.volume29en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherGalenos Publishing Houseen_US
dc.relation.ispartofDiagnostic and Interventional Radiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240825_Gen_US
dc.subjectArtificial intelligenceen_US
dc.subjectbreasten_US
dc.subjectcanceren_US
dc.subjecthigh risk lesion of breasten_US
dc.subjectimage-guided biopsyen_US
dc.subjectartificial intelligenceen_US
dc.subjectbreasten_US
dc.subjectbreast tumoren_US
dc.subjectdiagnostic imagingen_US
dc.subjectfemaleen_US
dc.subjecthumanen_US
dc.subjectimage guided biopsyen_US
dc.subjectpathologyen_US
dc.subjectproceduresen_US
dc.subjectretrospective studyen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectBreasten_US
dc.subjectBreast Neoplasmsen_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectImage-Guided Biopsyen_US
dc.subjectRetrospective Studiesen_US
dc.titlePrediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective studyen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu