{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T12:03:09Z","timestamp":1790078589120,"version":"4.0.1"},"reference-count":25,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: To quantitatively evaluate pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating prostate malignant lesions from normal appearing prostatic tissue, and to analyze their correlation with Gleason scores (GS). Methods: A retrospective analysis was performed on 51 patients with initial biopsy-confirmed prostate cancer who underwent multiparametric MRI (mpMRI) between May 2021 and April 2024 for elevated serum prostate-specific antigen (PSA) levels and\/or suspicious digital rectal examination (DRE) findings. Forty-three eligible patients with histopathologically confirmed prostate adenocarcinoma (GS \u2265 6) who met all strict imaging criteria were included. Demographic parameters, serum PSA, prostate volume, PSA density (manual: PSA-Dm, workstation: PSA-Dw), and lesion dimensions were recorded. Free-hand 2D regions of interest (ROIs) were placed on index lesions and normal peripheral zone tissue on a dedicated workstation (1.5-T MRI, syngo.via Tofts model) to compute quantitative and semi-quantitative pharmacokinetic parameters (Ktrans, Kep, iAUC, Ve, \u03c72). Statistical evaluations utilized non-parametric tests, Spearman\u2019s rank correlation, and clustered ROC analysis with patient-level bootstrapping. Results: Mean patient age was 64.21 \u00b1 8.82 years. Significant differences were identified across GS groups regarding serum PSA, PSA-Dm, and PSA-Dw (p &lt; 0.05). Group 3 (GS \u2265 8) demonstrated significantly higher median PSA (12.99 ng\/mL) compared to Groups 1 and 2 (p = 0.004). For predicting high-risk disease (GS \u2265 8), ROC analysis for PSA (cut-off 7.25 ng\/mL) yielded an area under the curve (AUC) of 0.798, whereas Ktrans performed poorly (AUC = 0.560). Conversely, for intra-patient paired comparisons distinguishing malignant lesions from normal background parenchyma, all quantitative DCE-MRI parameters (Ktrans, Kep, iAUC, Ve, \u03c72) were significantly higher in malignant lesions (all p &lt; 0.001, surviving stringent Bonferroni correction). Among pharmacokinetic parameters, Ktrans (cut-off: 0.105, AUC = 0.853, sensitivity: 81.4%, specificity: 76.7%) and \u03c72 (cut-off: 0.049, AUC = 0.844, sensitivity: 79.1%, specificity: 76.7%) exhibited the highest discriminative performance for identifying malignancy. Crucially, however, DCE-MRI parameters demonstrated no statistically significant correlation with Gleason scores (all p &gt; 0.05) and failed to independently predict high-grade disease. Conclusions: Quantitative DCE-MRI pharmacokinetic parameters effectively differentiate malignant prostate lesions from normal parenchyma within this specific cohort, but they do not correlate with histological grade. Furthermore, the derived diagnostic cut-offs are highly protocol-dependent, reflecting 1.5-T hardware and 2D ROI sampling. Robust cross-platform standardization and prospective 3D whole-gland validations are strictly required before these functional metrics can be reliably incorporated into clinical workflows.<\/jats:p>","DOI":"10.3390\/diagnostics16193075","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:09:35Z","timestamp":1790075375000},"page":"3075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantitative Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Perfusion in Multiparametric Prostate MRI: An Exploratory Intra-Patient Study Differentiating Malignant Lesions from Normal Tissue"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1707-8288","authenticated-orcid":false,"given":"Muhammed","family":"Sonkaya","sequence":"first","affiliation":[{"name":"Department of Radiology, Faculty of Medicine, Van Y\u00fcz\u00fcnc\u00fc Y\u0131l University, 65040 Van, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1429-077X","authenticated-orcid":false,"given":"\u0130lyas","family":"D\u00fcndar","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine, Van Y\u00fcz\u00fcnc\u00fc Y\u0131l University, 65040 Van, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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