Purpose: To assess the need of the dynamic contrast-enhanced (DCE) sequence in addition to T2-weighted imaging (T2-WI) and diffusion-weighted imaging (DWI) for the detection of clinically significant prostate cancer in the presence of artifacts associated with rectal gas (which compromise the diffusion assessment) and/or PIRADS 3 lesions.
Methods: This retrospective study was approved by the institutional review board; informed consent was not required. Patients referred consecutively over a period of 5 months for elevated PSA underwent multiparametric magnetic resonance imaging (mpMRI). mpMRI was performed using a 3T MRI system without an endorectal coil. The MRI findings were reviewed by two radiologists and were scored according to the Prostate Imaging Reporting and Data System version 2.0 (PI-RADSv2). Any discrepancies were resolved by consensus. For statistical purposes, lesions were classified as PIRADS 1-2, PIRADS 3, or PIRADS 4-5. First, all studies were reviewed using a biparametric assessment (T2-WI + DWI), and the presence or absence of susceptibility artifacts was assessed for each prostate. Subsequently, all images were analyzed using the standard multiparametric approach (T2-WI + DWI + DCE).
Results: The biparametric evaluation (T2-WI + DWI) showed artifacts (due to the presence of rectal gas or other) in 87 patients (43.5%) and no artifacts in 113 patients (56.5%). In the latter group, 15 patients had peripheral zone (PZ) PIRADS 3 lesions. Thus, a total of 102 patients (51%) had artifacts or PZ PIRADS 3 lesions and therefore required DCE. When evaluating the group of prostates without artifacts, 13.3% of prostates required DCE. A total of 17 (23.9%) PIRADS 4-5 prostate lesions would have not been detected without the use of DCE.
Conclusion: Biparametric evaluation of the prostate revealed some limitation due to the presence of artifacts or PIRADS 3 PZ lesions. Artifacts were present in almost 44% of our patients, but when the DWI was correctly evaluated, only 13.3% of prostates required DCE.
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http://dx.doi.org/10.1007/s00261-021-03011-0 | DOI Listing |
Prostate Cancer Prostatic Dis
January 2025
Northwestern University, Feinberg School of Medicine, Department of Urology, Chicago, IL, 60611, USA.
Background: Traditional nomograms can inform the presence of extraprostatic extension (EPE) but not laterality, which remains important for surgical planning, and have not fully incorporated multiparametric MRI data. We evaluated predictors of side-specific EPE on surgical pathology including MRI characteristics and developed side-specific EPE risk calculators.
Methods: This was a retrospective cohort of patients evaluated with mpMRI prior to radical prostatectomy (RP) in our eleven hospital healthcare system from July 2018-November 2022.
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).
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