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A retrospective cohort of 80 consecutive men with mpMRI and prostate biopsy under suspected prostate cancer underwent consensus interpretation and radiologist-performed magnetic resonance imaging texture analysis (MRTA). First-, second-, and higher-order texture parameters from mpMRI were compared and tested using univariate and multivariate logistic regression with ROC analysis. MRTA-derived models, especially ADC-based first-order parameters, showed high diagnostic performance for csPCa differentiation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/texture-analysis-of-multiparametric-magnetic-resonance-imaging-for-differentiating-clinically-significant-prostate-cancer-in-the-peripheral-zone-research-article/384964/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/texture-analysis-of-multiparametric-magnetic-resonance-imaging-for-differentiating-clinically-significant-prostate-cancer-in-the-peripheral-zone-research-article/384964.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn Holloway","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of texture analysis in this study?","Question",{"text":112,"@type":113},"To assess whether magnetic resonance imaging texture analysis (MRTA) adds tissue heterogeneity information that can help differentiate clinically significant prostate cancer in the peripheral zone on mpMRI.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were texture parameters obtained and evaluated?",{"text":117,"@type":113},"Two radiologists performed consensus interpretation and extracted first-, second-, and higher-order texture parameters from mpMRI, then used univariate and multivariate logistic regression and ROC curve analysis to identify independent predictors of csPCa.",{"name":119,"@type":110,"acceptedAnswer":120},"Which texture-based variables performed best for distinguishing csPCa?",{"text":121,"@type":113},"Univariate ROC analysis indicated ADC mean and ADC median as the best variables. The first-order logistic regression model based on ADC maps (mean + entropy) produced the highest AUC value among the tested texture-based models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},384964,1790342315,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","| | Turkish Journal of Medical Sciences [http://journals.tubitak.gov.tr/medical/](http://journals.tubitak.gov.tr/medical/) | Turk J Med Sci (2023) 53: 701-711 © TÜBİTAK |\n| --- | --- | --- |\n| | \u003Cbr>Research Article | doi:10.55730/1300-0144.5633 |\n\nTexture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zone  \nHalil ÖZER1,*􀁋, Mustafa KOPLAY1􀁋, Ahmet BAYTOK1􀁋, Nusret SEHER1􀁋, Lütfi Saltuk DEMİR2􀁋, Abidin KILINÇER1􀁋, Mehmet KAYNAR3􀁋, Serdar GÖKTAŞ3􀁋  \n1  \nDepartment of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey 2  \nDepartment of Public Health, Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey 3  \nDepartment of Urology, Faculty of Medicine, Selcuk University, Konya, Turkey  \nReceived: 31.07.2022  Accepted/Published Online: 01.02.2023  Final Version: 19.06.2023  \nBackground/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.  \nMaterials and methods: This retrospective study included 80 consecutive patients who had an mpMRI and a prostate biopsy for suspected 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.  \nResults: 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 \u003C 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 regression models (p \u003C 0.001) .  \nConclusion: 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.  \nKey words: Prostate cancer, texture analysis, magnetic resonance imaging, radiomics  \n1. Introduction  \nProstate cancer is the second most frequently diagnosed cancer in men and the fifth leading cause of death worldwide [1,2] . Diagnosing prostate cancer consists of digital rectal examination, assessing serum prostate-specific antigen (PSA) levels, and transrectal ultrasound (US)-guided prostate biopsy. However, there are some limitations to these methods. Serum PSA level has a low specificity (25%–40%) and can lead to unnecessary biopsies. Transrectal US-guided biopsy does not usually allow direct visualization of abnormal regions and focuses on the peripheral zone (PZ), missing clinically significant prostate cancer (csPCa) and overdiagnosing csPCa. The detection of csPCa and the evaluation of their biological aggressiveness are important for treatment [3-5] .  \n* [Correspondence: drhalilozer@gmail.com](Correspondence: drhalilozer@gmail.com)  \nMultiparametric magnetic resonance imaging (mpMRI) of the prostate is useful in detecting csPCa [5-9] . csPCa has been defined on histology as Gleason scores of ≥7 (including 3 + 4 with a prominent but not predominant Gleason 4 component), volume of ≥ 0.5 mL, or extra prostatic extension [8] . mpMRI based on the second version of the P","cbCaipei29FJrzRO","https://ap.wps.com/l/cbCaipei29FJrzRO","pdf",1209625,11,"English","# Background/aim\n# Materials and methods\n## Study design\n# Results\n# Conclusion\n# Key words","[{\"question\":\"What is the purpose of texture analysis in this study?\",\"answer\":\"To assess whether magnetic resonance imaging texture analysis (MRTA) adds tissue heterogeneity information that can help differentiate clinically significant prostate cancer in the peripheral zone on mpMRI.\"},{\"question\":\"How were texture parameters obtained and evaluated?\",\"answer\":\"Two radiologists performed consensus interpretation and extracted first-, second-, and higher-order texture parameters from mpMRI, then used univariate and multivariate logistic regression and ROC curve analysis to identify independent predictors of csPCa.\"},{\"question\":\"Which texture-based variables performed best for distinguishing csPCa?\",\"answer\":\"Univariate ROC analysis indicated ADC mean and ADC median as the best variables. The first-order logistic regression model based on ADC maps (mean + entropy) produced the highest AUC value among the tested texture-based models.\"}]","Texture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zone - Research Article | PDF",1790266125,28]