Combining Multiparametric MRI and PSA Density for Improved Diagnostic Accuracy in Prostate Cancer
Year: 2024
Type: article
Study design: Observational Study
Abstract: Objective: The objective of this research was to investigate the combined use of multiparametric magnetic resonance imaging (Mp-MRI) and prostate-specific antigen density (PSAD) to increase diagnostic accuracy in detecting prostate cancer (PCa) and to reduce unnecessary biopsies. Materials and Methods:This retrospective analysis included 399 patients who underwent prostate biopsy at Ankara City Hospital between 2021 and 2022, primarily due to clinical indications suggestive of PCa.The patient cohort was categorized into distinct groups according to their PSAD, with a defined threshold of 0.15 ng/mL/cc, and their respective prostate imaging reporting and data system (PI-RADS) scores; subsequently, the diagnostic performance metrics, including sensitivity, specificity, and predictive values for determining Pca, were meticulously evaluated across different combinations of PI-RADS classifications and PSAD levels.Results: Among the 399 patients, 37.6% had PCa and 16.8% had clinically significant PCa (csPca).Patients who exhibited PI-RADS scores of 3 or higher combined with a PSAD score of at least 0.15 ng/mL/cc exhibited the greatest positive predictive value, achieving 74.1% for overall PCa and 39.3% for csPca.The integration of PI-RADS assessment with PSAD thresholds notably enhanced diagnostic accuracy, leading to improved detection rates of clinically significant cases while concurrently minimizing the frequency of unnecessary biopsy procedures. Conclusion:The simultaneous application of Mp-MRI and PSAD enhances the precision of Pca diagnosis and serves as a valuable tool for reducing the need for unnecessary biopsies, especially in patients with PI-RADS scores of 3 or above accompanied by elevated PSAD levels.
Source: Bulletin of Urooncology
Authors: Ferhat Çelikkaleli, Cüneyt Özden, Süleyman Bulut, Cevdet Serkan Gökkaya, Binhan Kağan Aktaş, Yalçın Kızılkan, Samet Şenel, Süleyman Sağır
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405773948