Texture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zone

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dc.contributor.authorOzer, Halil
dc.contributor.authorKoplay, Mustafa
dc.contributor.authorBaytok, Ahmet
dc.contributor.authorSeher, Nusret
dc.contributor.authorDemir, Lutfi Saltuk
dc.contributor.authorKilincer, Abidin
dc.contributor.authorKaynar, Mehmet
dc.date.accessioned2024-02-23T14:41:45Z
dc.date.available2024-02-23T14:41:45Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractBackground/aim: Texture analysis (TA) provides additional tissue heterogeneity data that may assist in differentiating peripheral zone (PZ) lesions in multiparametric magnetic resonance imaging (mpMRI). This study investigates the role of magnetic resonance imaging texture analysis (MRTA) in detecting clinically significant prostate cancer (csPCa) in the PZ.Materials and methods: This retrospective study included 80 consecutive patients who had an mpMRI and a prostate biopsy for sus-pected prostate cancer. Two radiologists in consensus interpreted mpMRI and performed texture analysis based on their histopathology. The first-, second-, and higher-order texture parameters were extracted from mpMRI and were compared between groups. Univariate and multivariate logistic regression analyses were performed using the texture parameters to determine the independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance of the texture parameters.Results: In the periferal zone, 39 men had csPCa, while 41 had benign lesions or clinically insignificant prostate cancer (cisPCa). The majority of texture parameters showed statistically significant differences between the groups. Univariate ROC analysis showed that the ADC mean and ADC median were the best variables in differentiating csPCa (p < 0.001). The first-order logistic regression model (mean + entropy) based on the ADC maps had a higher AUC value (0.996; 95% CI: 0.989-1) than other texture-based logistic regres-sion models (p < 0.001).Conclusion: MRTA is useful in differentiating csPCa from other lesions in the PZ. Consequently, the first-order multivariate regression model based on ADC maps had the highest diagnostic performance in differentiating csPCa.en_US
dc.identifier.doi10.55730/1300-0144.5633
dc.identifier.endpage711en_US
dc.identifier.issn1300-0144
dc.identifier.issn1303-6165
dc.identifier.issue3en_US
dc.identifier.pmid37476894en_US
dc.identifier.scopus2-s2.0-85163692172en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage701en_US
dc.identifier.urihttps://doi.org/10.55730/1300-0144.5633
dc.identifier.urihttps://hdl.handle.net/20.500.12452/16978
dc.identifier.volume53en_US
dc.identifier.wosWOS:001022334700010en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Medical Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectProstate Canceren_US
dc.subjectTexture Analysisen_US
dc.subjectMagnetic Resonance Imagingen_US
dc.subjectRadiomicsen_US
dc.titleTexture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zoneen_US
dc.typeArticleen_US

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