Objectives: To assess the utility of multiparametric MRI in detecting clinically significant prostate cancer (csPCa) by comparing PI-RADSv2 scores with International Society of Urological Pathology (ISUP) pathologic grading criteria.
Methods: Data from 137 patients were retrospectively analyzed. PI-RADSv2 scores were compared with pathologic grade using ISUP criteria. Pathologic grades were divided into clinically significant (groups 3-5) and clinically insignificant lesions (groups 1-2). Chi-squared analysis was performed for to assess correlation.
Results: Sensitivity and specificity of PI-RADSv2 score 3-5 lesions for detecting csPCa was 100% and 18.5%, respectively. Negative predictive value (NPV) is 100% for these lesions. When considering only PI-RADSv2 score 4-5 lesions, sensitivity decreases to 90% and specificity increases to 67.5%, with a NPV of 98.5%. When only PI-RADSv2 score 5 lesions are considered, sensitivity decreases to 50% and specificity increases to 90%, with a NPV of 95%.
Conclusions: Multiparametric MRI has excellent sensitivity for detecting csPCa. Specificity is poor for PI-RADSv2 score 3 lesions but improves significantly for PI-RADSv2 score 4 and 5 lesions. Overall, mpMRI is an excellent screening tool for csPCa, as designated by the recently validated ISUP criteria.
Advances In Knowledge: Multiple limitations of the longstanding Gleason pathologic scoring system have led to the development of new ISUP pathologic criteria, which is more focused on the clinical significance of lesions. There are currently insufficient studies evaluating and validating the ISUP criteria with PIRADS v2 evaluation of the prostate.
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http://dx.doi.org/10.1067/j.cpradiol.2019.06.010 | DOI Listing |
Acad Radiol
December 2024
Department of Nuclear Medicine, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine, University of Zurich, Baden, Switzerland; Department of Health Science and Technology ETH Zurich, Zurich, Switzerland; Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Rationale And Objectives: Multiparametric MRI (mpMRI) substantially improves the detection of significant prostate carcinoma (PCa) compared to systematic biopsy. Nevertheless, mpMRI can overlook aggressive forms of PCa. Recent studies showed, that infiltrative growth (INF) has less restricted diffusion.
View Article and Find Full Text PDFUrol Oncol
October 2024
Department of Urology, Yale University School of Medicine, New Haven, CT. Electronic address:
Importance: The Prostate Imaging Reporting & Data System (PI-RADS) scoring guidelines were developed to address the substantial variation in interpretation and reporting of prostate cancer (PCa) multiparametric MRI (mpMRI) results, and subsequent updates have sought to further improve inter-reader reliability. Nonetheless, the variability of PI-RADS scoring in real-world settings may represent a continuing challenge to the widespread standardization of prostate mpMRI and limit its overall clinical benefit.
Objective: To assess variability in mpMRI interpretation and reporting of PCa, we evaluated the discrepancies in PI-RADS scoring between community practices and a tertiary academic care center.
Eur Radiol
November 2024
Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.
Objectives: To establish and evaluate an ultra-fast MRI screening protocol for prostate cancer (PCa) in comparison to the standard multiparametric (mp) protocol, reducing scan time and maintaining adequate diagnostic performance.
Materials And Methods: This prospective single-center study included consecutive biopsy-naïve patients with suspected PCa between December 2022 and March 2023. A PI-RADSv2.
Quant Imaging Med Surg
May 2024
Siemens Healthineers GmbH, Erlangen, Germany.
Background: Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa.
View Article and Find Full Text PDFEur Urol Oncol
October 2024
Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; LabTau, INSERM Unit 1032, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France. Electronic address:
Background And Objective: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI.
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