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A retrospective cohort of 189 PI-RADS 5 lesions underwent 3.0-T MRI, with lesions manually segmented on ADC maps and texture features extracted using FireVoxel. A combined clinical-plus-ADC texture model achieved AUC 93.1%, with SEN 98.7%, SPE 60.0%, PPV 97.9%, and NPV 71.6%, showing net benefit for biopsy guidance.",{"@graph":69,"@context":127},[70,84,106],{"@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-enhances-diagnostic-accuracy-of-lesions-scored-as-5-in-the-prostate-imaging-reporting-and-data-system-in-magnetic-resonance-imaging/376524/",{"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-enhances-diagnostic-accuracy-of-lesions-scored-as-5-in-the-prostate-imaging-reporting-and-data-system-in-magnetic-resonance-imaging/376524.png","ImageObject",300,407,{"name":92,"@type":93},"CatatanPagi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address in PI-RADS 5 lesions?","Question",{"text":113,"@type":114},"PI-RADS 5 lesions have a notable false-positive rate. 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ORIGINAL)[ ORIGINAL](Available online at dirjournal.org. ORIGINAL) ARTICLE  \nContent of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  \nTexture analysis enhances diagnostic accuracy of lesions scored as 5 in the Prostate Imaging Reporting and Data System in magnetic resonance imaging  \n Yan Bai 1-3  \n Xin Ru Xie3  \n Ying Hou 1,3  \nYu Dong Zhang 1,3  Hai Bin Shi1,3*  Chen Jiang Wu 1,3*  \n1The First Affiliated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, China 2The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China  \n3School of Medical Imaging, Nanjing Medical University, Nanjing, China  \n* There authors contributed equally to this work Corresponding author: Chen Jiang Wu  \nE-mail: [njmu_wcj@163.com](njmu_wcj@163.com)  \nReceived 08 September 2025; revision requested 05 October 2025; accepted 08 November 2025.  \nEpub: 01.12.2025  \nPublication date: 02.03.2026  \nDOI: 10.4274/dir.2025.253640  \nPURPOSE  \nProstatitis is frequently observed in false-positive lesions scored as 5 in the Prostate Imaging Reporting and Data System (PI-RADS), necessitating improved diagnostic tools. This study investigated the potential of magnetic resonance imaging (MRI) texture analysis of apparent diffusion coefficient (ADC) sequences to enhance the differentiation of prostatitis from prostate cancer (PCa) in PI-RADS 5 lesions.  \nMETHODS  \nThis retrospective study enrolled patients undergoing 3.0-T MRI with lesions scored as PI-RADS 5. Lesions were manually delineated on ADC maps, and texture features were extracted using FireVoxel. Clinical data and ADC texture parameters were collected. The diagnostic performance [area under the curve (AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV)] of the clinical data, ADC texture, and a combined model were calculated and compared using the DeLong test.  \nRESULTS  \nThe final cohort included 189 patients with 189 PI-RADS 5 lesions (164 PCa, 25 prostatitis) . The combined model, incorporating clinical indicators (age, prostate-specific antigen density) and ADC texture parameters (signal coefficient of variation, ADC percentile), revealed the optimal diagnostic performance: SEN 98.7%, SPE 60.0%, PPV 97.9%, NPV 71.6%, and AUC 93.1%. Bootstrap resampling verified the robustness of the model. Decision curve analysis indicated an improved net benefit with the combined model for guiding biopsy decisions.  \nCONCLUSION  \nADC imaging texture parameters are valuable for the differential diagnosis of prostatitis from lesions scored as PI-RADS 5. Their combination with clinical indicators substantially improves diagnostic performance, providing valuable information to facilitate surgical decision-making and potentially reduce unnecessary biopsies.  \nCLINICAL SIGNIFICANCE  \nThis study addresses a critical limitation of the current PI-RADS system, which exhibits a notable rate of false positives in high-risk PI-RADS 5 lesions. By demonstrating the added value of quantitative ADC texture analysis in this specific diagnostic challenge, this research offers a practical and potentially translatable approach to reducing the number of unnecessary biopsies for PI-RADS 5 lesions.  \nKEYWORDS  \nProstate cancer, prostatitis, PI-RADS 5, magnetic resonance imaging, diffusion-weighted imaging, apparent diffusion coefficient, texture analysis, differential diagnosis  \nYou may cite this article as: Bai Y, Xie XR, Hou Y, Zhang YD, Shi HB, Wu CJ. Texture analysis enhances diagnostic accuracy of lesions scored as 5 in the Prostate Imaging Reporting and Data System in magnetic resonance imaging. Diagn Interv Radiol. 2026;32(2):156-163 .  \n156  \nAdvances in magnetic resonance im  \naging (MRI) have substantially en  \nhanced the detection ","cbCaidP1KTH8l4bF","https://ap.wps.com/l/cbCaidP1KTH8l4bF","pdf",1528760,"English","# PURPOSE\n# METHODS\n# RESULTS\n# CONCLUSION\n# CLINICAL SIGNIFICANCE\n# KEYWORDS","[{\"question\":\"What problem does the study address in PI-RADS 5 lesions?\",\"answer\":\"PI-RADS 5 lesions have a notable false-positive rate. The study focuses on distinguishing prostatitis from prostate cancer to reduce unnecessary biopsies.\"},{\"question\":\"How were MRI texture features obtained?\",\"answer\":\"Patients underwent 3.0-T MRI, lesions were manually delineated on ADC maps, and texture features were extracted using FireVoxel. Clinical data and ADC texture parameters were then collected for modeling.\"},{\"question\":\"How did the combined model perform compared with its components?\",\"answer\":\"The combined model integrating clinical indicators and ADC texture parameters produced the best diagnostic performance, with AUC 93.1% and sensitivity of 98.7%, along with strong positive predictive value (97.9%).\"},{\"question\":\"What is the clinical significance of adding ADC texture analysis?\",\"answer\":\"ADC texture parameters help improve differentiation of prostatitis in PI-RADS 5 lesions. Combining them with clinical indicators supports biopsy decision-making and may reduce unnecessary biopsies.\"}]","Texture analysis enhances diagnostic accuracy of lesions scored as 5 in the Prostate Imaging Reporting and Data System in magnetic resonance imaging | PDF",1790219727]