Publication:
Unenhanced CT texture analysis of clear cell renal cell carcinomas: a machine learning-based study for predicting histopathologic nuclear grade

dc.contributor.coauthorKocak, Burak
dc.contributor.coauthorDurmaz, Emine Sebnem
dc.contributor.coauthorAtes, Ece
dc.contributor.coauthorKilickesmez, Ozgur
dc.contributor.departmentKUH (Koç University Hospital)
dc.contributor.kuauthorKaya, Özlem Korkmaz
dc.contributor.schoolcollegeinstituteKUH (KOÇ UNIVERSITY HOSPITAL)
dc.date.accessioned2024-11-09T23:35:33Z
dc.date.issued2019
dc.description.abstractOBJECTIVE. The purpose of this study is to investigate the predictive performance of machine learning (ML)-based unenhanced CT texture analysis in distinguishing low (grades I and II) and high (grades III and IV) nuclear grade clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS. For this retrospective study, 81 patients with clear cell RCC (56 high and 25 low nuclear grade) were included from a public database. Using 2D manual segmentation, 744 texture features were extracted from unenhanced CT images. Dimension reduction was done in three consecutive steps: reproducibility analysis by two radiologists, collinearity analysis, and feature selection. Models were created using artificial neural network (ANN) and binary logistic regression, with and without synthetic minority oversampling technique (SMOTE), and were validated using 10-fold cross-validation. The reference standard was histopathologic nuclear grade (low vs high). RESULTS. Dimension reduction steps yielded five texture features for the ANN and six for the logistic regression algorithm. None of clinical variables was selected. ANN alone and ANN with SMOTE correctly classified 81.5% and 70.5%, respectively, of clear cell RCCs, with AUC values of 0.714 and 0.702, respectively. The logistic regression algorithm alone and with SMOTE correctly classified 75.3% and 62.5%, respectively, of the tumors, with AUC values of 0.656 and 0.666, respectively. The ANN performed better than the logistic regression (p < 0.05). No statistically significant difference was present between the model performances created with and without SMOTE (p> 0.05). CONCLUSION. ML-based unenhanced CT texture analysis using ANN can be a promising noninvasive method in predicting the nuclear grade of clear cell RCCs.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue6
dc.description.openaccessNO
dc.description.sponsoredbyTubitakEuN/A
dc.description.volume212
dc.identifier.doi10.2214/AJR.18.20742
dc.identifier.eissn1546-3141
dc.identifier.issn0361-803X
dc.identifier.scopus2-s2.0-85067547462
dc.identifier.urihttps://doi.org/10.2214/AJR.18.20742
dc.identifier.urihttps://hdl.handle.net/20.500.14288/12521
dc.identifier.wos468598300002
dc.keywordsClear cell renal cell carcinoma
dc.keywordsCT
dc.keywordsMachine learning
dc.keywordsNuclear grade
dc.keywordsRadiomics
dc.keywordsDiagnostic-accuracy
dc.keywordsPercutaneous biopsy
dc.keywordsModel
dc.keywordsMasses
dc.keywordsSize
dc.keywordsMetaanalysis
dc.keywordsInformation
dc.keywordsSelection
dc.keywordsImages
dc.keywordsStage
dc.language.isoeng
dc.publisherAmer Roentgen Ray Soc
dc.relation.ispartofAmerican Journal Of Roentgenology
dc.subjectRadiology
dc.subjectNuclear medicine
dc.subjectMedical imaging
dc.titleUnenhanced CT texture analysis of clear cell renal cell carcinomas: a machine learning-based study for predicting histopathologic nuclear grade
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorKaya, Özlem Korkmaz
local.publication.orgunit1KUH (KOÇ UNIVERSITY HOSPITAL)
local.publication.orgunit2KUH (Koç University Hospital)
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relation.isParentOrgUnitOfPublication055775c9-9efe-43ec-814f-f6d771fa6dee
relation.isParentOrgUnitOfPublication.latestForDiscovery055775c9-9efe-43ec-814f-f6d771fa6dee

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