Evidence map›Paper›PMID 41324144›Full record

ArticleDiagnostic and interventional radiology (Ankara, Turkey)2026

Texture analysis enhances diagnostic accuracy of lesions scored as 5 in the Prostate Imaging Reporting and Data System in magnetic resonance imaging.

Yan Bai, Xin Ru Xie, Ying Hou, Yu Dong Zhang, Hai Bin Shi, Chen Jiang Wu

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In one paragraph

Article in Diagnostic and interventional radiology (Ankara, Turkey), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yan BaiThe First Affi liated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, China.
Xin Ru XieSchool of Medical Imaging, Nanjing Medical University, Nanjing, China
Ying HouThe First Affi liated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, China.
Yu Dong ZhangThe First Affi liated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, China.
Hai Bin ShiThe First Affi liated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, China.
Chen Jiang WuThe First Affi liated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeProstatitis 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.

methodsThis 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.

resultsThe 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.

conclusionADC 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. CLINICAL SIGNIFICANCE: This 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.

Indexed as

Magnetic Resonance ImagingProstateProstatic NeoplasmsProstatitisAgedDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesSensitivity and Specificityapparent diffusion coefficientdifferential diagnosisdiffusion-weighted imagingmagnetic resonance imagingPI-RADS 5Prostate cancerprostatitistexture analysis

Identifiers

PMID41324144
PMCPMC12954128

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.