ArticleRadiology. Imaging cancer2025
Quantitative 3-T Multiparametric MRI Parameters as Predictors of Aggressive Prostate Cancer.
Article in Radiology. Imaging cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Development and internal validation of a noninvasive predictive model based on iron and fat measurements for pathologic risk stratification in prostate cancer.Translational andrology and urology · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Purpose To determine which quantitative 3-T multiparametric MRI (mpMRI) parameters correlate with and help predict the presence of aggressive large cribriform pattern (LCP) and intraductal carcinoma (IDC) prostate cancer (PCa) at whole-mount histopathology (WMHP). Materials and Methods This retrospective study included 130 patients (mean age ± SD, 62.6 years ± 7.2; 100% male) with 141 PCa lesions who underwent preoperative prostate 3-T mpMRI, radical prostatectomy, and WMHP between January 2019 and December 2022. Lesions at WMHP were matched to 3-T mpMRI lesions with American College of Radiology Prostate Imaging Reporting and Data System version 2.1 scores of at least 3 or higher, and the following parameters were derived: apparent diffusion coefficient (ADC), volume transfer constant, rate constant, and initial area under the curve (iAUC). Each lesion was categorized into three subcohorts with increasing aggressiveness: LCP negative and IDC negative (subcohort 1), LCP positive and IDC negative (subcohort 2), and LCP positive and IDC negative (subcohort 3). Analysis of variance was performed to assess differences, Jonckheere test was performed to establish trends, and a classification and regression tree (CART) was used to establish a prediction model. Results Of the 141 total lesions, there were 41 (29.1%), 49 (34.8%), and 51 (36.2%) lesions in subcohorts 1, 2, and 3, with mean ADCs of 892 × 10
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