The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (K), reverse volume transfer constant (k), plasma volume fraction (V), extravascular extracellular space volume fraction (V), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (K, k, V, and V), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that V values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, k, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.
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http://dx.doi.org/10.2214/AJR.20.22843 | DOI Listing |
Urol Oncol
January 2025
Research Department, Urovallarta Medical Center, Puerto Vallarta, Mexico.
Background: Multiparametric MRI (Mp-MRI) is a key tool to screen for Prostate Cancer (Pca) and Clinically Significant Prostate Cancer (CsPca). It primarily includes T2-Weighted imaging (T2w), diffusion-weighted imaging (DWI), and Dynamic Contrast-Enhanced imaging (DCE). Despite its improvements in CsPca screening, concerns about the cost-effectiveness of DCE persist due to its associated side effects, increased cost, longer acquisition time, and limitations in patients with poor kidney function.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China (B.Z., F.M., X.S., S.L., Q.W.); Department of Urology, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, Guangdong 510080, China (Q.W.). Electronic address:
Rationale And Objectives: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL.
Materials And Methods: A total of 1124 patients with histological results and PSA levels between 4 and 10 ng/mL were enrolled from one public dataset and two local institutions. An nnUNet was trained for prostate masks, and a feature extraction module identified suspicious lesion masks.
Sci Rep
January 2025
Department of MRI, Zhongshan City People's Hospital, No. 2, Sunwen East Road, Shiqi District, Zhongshan, 528403, Guangdong, China.
To investigate the potential of an MRI-based radiomic model in distinguishing malignant prostate cancer (PCa) nodules from benign prostatic hyperplasia (BPH)-, as well as determining the incremental value of radiomic features to clinical variables, such as prostate-specific antigen (PSA) level and Prostate Imaging Reporting and Data System (PI-RADS) score. A restrospective analysis was performed on a total of 251 patients (training cohort, n = 119; internal validation cohort, n = 52; and external validation cohort, n = 80) with prostatic nodules who underwent biparametric MRI at two hospitals between January 2018 and December 2020. A total of 1130 radiomic features were extracted from each MRI sequence, including shape-based features, gray-level histogram-based features, texture features, and wavelet features.
View Article and Find Full Text PDFInt Urol Nephrol
January 2025
Faculty of Medical Sciences, Pharmacology and Toxicology Department, University of Kragujevac, Kragujevac, Serbia.
Purposes: Intermediate-risk prostate cancer (IR PCa) is the most common risk group for localized prostate cancer. This study aimed to develop a machine learning (ML) model that utilizes biopsy predictors to estimate the probability of IR PCa and assess its performance compared to the traditional clinical model.
Methods: Between January 2017 and December 2022, patients with prostate-specific antigen (PSA) values of ≤ 20 ng/mL underwent transrectal ultrasonography-guided prostate biopsies.
Radiol Imaging Cancer
January 2025
From the Departments of Radiological Sciences (D.H.S.K., I.S., V.M., W.H., K.H.S., D.S.L., S.S.R.), Medicine Statistics Core (T.G.), Pathology (A.S.), and Urology (R.E.R., S.S.R.), David Geffen School of Medicine at UCLA, 885 Tiverton Dr, Los Angeles, CA 90095.
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.
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