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MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status

dc.contributor.authorUcar, Nese
dc.contributor.authorBektas, Sibel
dc.contributor.authorKocak, Burak
dc.contributor.authorAkan, Yesim Namdar
dc.contributor.authorYildirim, Emine
dc.contributor.authorKAYADİBİ, YASEMİN
dc.date.accessioned2022-07-04T16:19:34Z
dc.date.available2022-07-04T16:19:34Z
dc.identifier.citationKAYADİBİ Y., Kocak B., Ucar N., Akan Y. N. , Yildirim E., Bektas S., "MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status", ACADEMIC RADIOLOGY, cilt.29, 2022
dc.identifier.issn1076-6332
dc.identifier.othervv_1032021
dc.identifier.otherav_df3ed40d-ca5e-4cc9-87ef-279569f51b59
dc.identifier.urihttp://hdl.handle.net/20.500.12627/185023
dc.identifier.urihttps://doi.org/10.1016/j.acra.2021.10.026
dc.description.abstractRationale and Objectives: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in patients with BC, using preoperative MRI images.
dc.language.isoeng
dc.subjectRadiology, Nuclear Medicine and Imaging
dc.subjectRadiological and Ultrasound Technology
dc.subjectHealth Sciences
dc.subjectDahili Tıp Bilimleri
dc.subjectNükleer Tıp
dc.subjectSağlık Bilimleri
dc.subjectTıp
dc.subjectKlinik Tıp (MED)
dc.subjectKlinik Tıp
dc.subjectRADYOLOJİ, NÜKLEER TIP ve MEDİKAL GÖRÜNTÜLEME
dc.titleMRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status
dc.typeMakale
dc.relation.journalACADEMIC RADIOLOGY
dc.contributor.departmentİstanbul Üniversitesi-Cerrahpaşa , Cerrahpaşa Tıp Fakültesi , Dahili Tıp Bilimleri Bölümü
dc.identifier.volume29
dc.contributor.firstauthorID3395287


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