Background: Current prostate biopsy (PBx) protocol for prostate cancer (PCa) diagnosis is to perform systematic biopsies (SBx) combined with targeted biopsies (TBx) in case of positive MRI (i.e. PI-RADS ≥ 3). To assess the utility of performing SBx in combination with TBx, we determined the added value of SBx brought to the diagnosis of PCa according to their sextant location and MRI target characteristics.
Methods: In our local prospectively collected database, we conducted a single-center retrospective study including all patients with a suspicion of PCa, who underwent transrectal ultrasound-guided (TRUS) prostate biopsies (PBx) with a prior MRI and a single lesion classified as PI-RADS ≥ 3. We have characterized the SBx according to their location on MRI: same sextant (S-SBx), adjacent sextant (A-SBx), ipsilateral side (I-SBx) and contralateral side (C-SBx). The added value of SBx and TBx was defined as any upgrading to significant PCa (csPCa) (ISUP ≥2).
Results: 371 patients were included in the study. The added value of SBx was 10% overall. Regarding the lesion location and the SBx sextant, the added value of SBx was: 5.1% for S-SBx, 5.4% for A-SBx, 4.9% for I-SBx and 1.9% for C-SBx. The overall added value of SBx was 6.8% for PI-RADS 3 lesions, 14% for PI-RADS 4 lesions and 6.7% for PI-RADS 5 lesions (p = 0.063). The added value of SBx for contralateral side was 1.9% (2/103), 3.1% (5/163) and 0% (0/105) for PI-RADS 3, PI-RADS 4 and PI-RADS 5 lesions, respectively (p = 0,4). The added value of SBx was lower when the number of TBx was higher (OR 0.57; CI 95% 0.37-0.85; p = 0.007).
Conclusions: Our results suggest that the utility of performing SBx in the contralateral lobe toward the MRI lesion was very low, supporting that they might be avoided.
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http://dx.doi.org/10.1038/s41391-023-00770-3 | DOI Listing |
Radiol Artif Intell
January 2025
https://www.procancer-i.eu/.
Purpose To assess the impact of scanner manufacturer and scan protocol on the performance of deep learning models to classify prostate cancer (PCa) aggressiveness on biparametric MRI (bpMRI). Materials and Methods In this retrospective study, 5,478 cases from ProstateNet, a PCa bpMRI dataset with examinations from 13 centers, were used to develop five deep learning (DL) models to predict PCa aggressiveness with minimal lesion information and test how using data from different subgroups-scanner manufacturers and endorectal coil (ERC) use (Siemens, Philips, GE with and without ERC and the full dataset)-impacts model performance. Performance was assessed using the area under the receiver operating characteristic curve (AUC).
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
Purpose: To develop novel nomograms for predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in patients with prostate-specific antigen (PSA) < 10 ng/ml and PI-RADS v2.1 score ≤ 3.
Methods: We retrospectively collected data from 327 men with PSA < 10 ng/ml and PI-RADS score ≤ 3 from June 2020 to June 2024 in our hospital.
Quant Imaging Med Surg
January 2025
Department of Nuclear Medicine, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background: Although F-prostate-specific membrane antigen-1007 (F-PSMA-1007) positron emission tomography/computed tomography (PET/CT) and multiparametric magnetic resonance imaging (mpMRI) are good predictors of prostate cancer (PCa) prognosis, their combined ability to predict prostate-specific antigen (PSA) persistence has not been thoroughly evaluated. In this study, we assessed whether clinical, mpMRI, and F-PSMA-1007 PET/CT characteristics could predict PSA persistence in patients with PCa treated with radical prostatectomy (RP).
Methods: This retrospective study involved consecutive patients diagnosed with PCa who underwent both preoperative mpMRI and PSMA PET/CT scans between April 2019 and June 2022.
Radiol Imaging Cancer
January 2025
From the Department of Radiology (A.C., A.N.Y., R.E., C.H., G.L., M.M., E.B.J., A.L.C., B.G., G.S.K., A.O.), Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy (A.C., A.N.Y., M.M., A.L.C., B.G.), Department of Surgery, Section of Urology (G.G., L.F.R., P.K.M., S.E.), Department of Pathology (T.A.), and Department of Public Health Sciences (M.G.), University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.
Purpose To evaluate the use of an automated hybrid multidimensional MRI (HM-MRI)-based tool to prospectively identify prostate cancer targets before MRI/US fusion biopsy in comparison with Prostate Imaging and Reporting Data System (PI-RADS)-based multiparametric MRI (mpMRI) evaluation by expert radiologists. Materials and Methods In this prospective clinical trial (ClinicalTrials.gov registration no.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Urology, The Second Hospital of Tianjin Medical University, No. 23 Pingjiang Road, Hexi Destrict, Tianjin, 300211, China.
To develop and validate biopsy-free nomograms to more accurately predict clinically significant prostate cancer (csPCa) in biopsy-naïve men with prostate imaging reporting and data system (PI-RADS) ≥ 4 lesions. A cohort of 931 patients with PI-RADS ≥ 4 lesions, undergoing prostate biopsies or radical prostatectomy from January 2020 to August 2023, was analyzed. Various clinical variables, including age, prostate-specific antigen (PSA) levels, prostate volume (PV), PSA density (PSAD), prostate health index (PHI), and maximum standardized uptake values (SUVmax) from PSMA PET-CT imaging, were assessed for predicting csPCa.
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