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Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status

dc.contributor.authorAtes, Ece
dc.contributor.authorKocak, Burak
dc.contributor.authorUlusan, Melis Baykara
dc.contributor.authorDurmaz, Emine Sebnem
dc.date.accessioned2021-03-04T15:00:38Z
dc.date.available2021-03-04T15:00:38Z
dc.date.issued2019
dc.identifier.citationKocak B., Durmaz E. S. , Ates E., Ulusan M. B. , "Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status", AMERICAN JOURNAL OF ROENTGENOLOGY, cilt.212, sa.3, 2019
dc.identifier.issn0361-803X
dc.identifier.othervv_1032021
dc.identifier.otherav_8488d628-a9d2-43ea-b1b7-d03df00b888f
dc.identifier.urihttp://hdl.handle.net/20.500.12627/90143
dc.identifier.urihttps://doi.org/10.2214/ajr.18.20443
dc.description.abstractOBJECTIVE. The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patients with clear cell renal cell carcinoma (RCC).
dc.language.isoeng
dc.subjectRADYOLOJİ, NÜKLEER TIP ve MEDİKAL GÖRÜNTÜLEME
dc.subjectKlinik Tıp
dc.subjectKlinik Tıp (MED)
dc.subjectTıp
dc.subjectDahili Tıp Bilimleri
dc.subjectSağlık Bilimleri
dc.subjectNükleer Tıp
dc.titleRadiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status
dc.typeMakale
dc.relation.journalAMERICAN JOURNAL OF ROENTGENOLOGY
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.identifier.volume212
dc.identifier.issue3
dc.contributor.firstauthorID262954


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