Prostate cancer (PCa) diagnosis using multiparametric magnetic resonance imaging (mpMRI) remains challenging, especially in Prostate Imaging Reporting and Data System 3 (PI-RADS 3) lesions, which present an intermediate risk of malignancy. This study aims to evaluate the diagnostic efficacy of various radiological parameters in PI-RADS 3 lesions to improve the decision-making process for prostate biopsies.This retrospective study included 76 patients with PI-RADS 3 lesions who underwent mpMRI and transrectal prostate biopsy at a tertiary university hospital between 2015 and 2022. Radiological parameters such as signal intensity, lesion size, border definition, morphological features, lesion location, and prostate volume were analyzed. Apparent diffusion coefficient (ADC) values and the patients' clinical data including age, prostate-specific antigen (PSA), and histopathological findings were also evaluated. Results: Among the 76 patients meeting the inclusion criteria, prostate cancer was detected in 17, with only one case being clinically significant (csPCa). Factors increasing malignancy risk in PI-RADS 3 lesions included poorly defined lesion borders, ADC values below 1180 μm²/sec, and prostate volume below 50.5 cc. The study highlighted the need for additional radiological and clinical parameters in the risk classification of PI-RADS 3 cases.This retrospective study included 76 patients with PI-RADS 3 lesions who underwent mpMRI and transrectal prostate biopsy at a tertiary university hospital between 2015 and 2022. Radiological parameters such as signal intensity, lesion size, border definition, morphological features, lesion location, and prostate volume were analyzed. Apparent diffusion coefficient (ADC) values and the patients' clinical data including age, prostate-specific antigen (PSA), and histopathological findings were also evaluated.Among the 76 patients meeting the inclusion criteria, prostate cancer was detected in 17, with only one case being clinically significant (csPCa). Factors increasing malignancy risk in PI-RADS 3 lesions included poorly defined lesion borders, ADC values below 1180 μm²/sec, and prostate volume below 50.5 cc. The study highlighted the need for additional radiological and clinical parameters in the risk classification of PI-RADS 3 cases.The findings suggest that incorporating additional radiological parameters into the evaluation of PI-RADS 3 lesions can enhance the accuracy of prostate cancer diagnosis. This approach could minimize unnecessary biopsies and ensure that significant malignancies are not overlooked. Future multicenter, large-scale studies are recommended to establish more definitive risk stratification criteria. · The study emphasizes the complexity of diagnosing prostate cancer in PI-RADS 3 lesions and the importance of detailed radiological assessment.. · It highlights the significance of specific radiological parameters, including lesion border definition and ADC values, in predicting malignancy.. · The research provides valuable insight for clinicians in order to make informed decisions regarding prostate biopsies, particularly in ambiguous PI-RADS 3 cases.. · Mersinlioğlu İ, Keven A, Tezel ZE et al. Enhancing Prostate Cancer Detection in PI-RADS 3 Cases: An In-depth Analysis of Radiological Indicators from Multiparametric MRI. Fortschr Röntgenstr 2024; DOI 10.1055/a-2374-2531.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1055/a-2374-2531 | DOI Listing |
J Comput Assist Tomogr
November 2024
From the Department of Radiology, Mayo Clinic, Rochester, MN.
Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)-related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance.
Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa.
Fr J Urol
January 2025
Department of Urology, North Hospital, AP-HM, Marseille, France.
Introduction: A significant proportion of newly diagnosed prostate cancer (PCa) cases are slow growing with a low risk of metastatic progression. There is a lack of data concerning the optimal biopsy regimen for improving diagnosis yield in PI-RADS3 lesions. This study aimed to assess the diagnostic value of current biopsy regimens in PI-RADS 3 lesions and identify clinical predictors to improve clinically significant PCa (csPCa) detection.
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.
BMC Med Imaging
December 2024
Department of MRI, Xinxiang Central Hospital (The Fourth Clinical College of Xinxiang Medical University), 56 Jinsui Road, Xinxiang, Henan, 453000, China.
Background: To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict clinically significant prostate cancer (csPCa, Gleason score ≥ 3 + 4) and avoid unnecessary biopsies.
Methods: This study retrospectively analyzed 350 patients with suspicious prostate lesions from our institution who underwent 3.0 Tesla multiparametric magnetic resonance imaging (mpMRI) prior to biopsy (training set, n = 191, testing set, n = 83, and a temporal validation set, n = 76).
Ethiop J Health Sci
October 2024
Department of Radiology, School of Medicine, Addis Ababa University, Addis Ababa, Ethiopia.
Background: Prostate cancer is a leading cause of cancer-related mortality among men, second only to lung cancer. Prostate magnetic resonance imaging (MRI) utilizing the Prostate Imaging and Reporting Data System (PI-RADS) v2.1 scoring system effectively stratifies patients by risk and correlates significantly with histopathological outcomes.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!