Search Results (118)

Prostate cancer is the most commonly diagnosed malignancy in men and continues to be a leading cause of cancer-related mortality. Accurate and timely diagnosis is essential for distinguishing clinically significant tumors from indolent lesions and for informing treatment decisions. Multiparametric magnetic resonance imaging (mpMRI) has revolutionized prostate cancer detection by enabling precise lesion localization, risk stratification, and improved biopsy targeting. Fusion biopsy, which combines mpMRI findings with real-time transrectal ultrasonography (TRUS), has emerged as a highly effective method for sampling suspicious lesions. This review provides an integrated anatomical, epidemiological, technical, and clinical overview that highlights the evolving role of fusion biopsy in contemporary prostate cancer diagnostics. We also explore emerging strategies such as penumbra-targeted sampling, discuss ongoing clinical challenges, and examine the impact of biopsy underestimation and lack of standardization. Compared to conventional systematic biopsy, mpMRI-TRUS fusion biopsy improves the detection of clinically significant prostate cancer while reducing the overdiagnosis of low-risk tumors. To our knowledge, few recent reviews have comprehensively synthesized current clinical guidelines, emerging biopsy techniques, and future directions within a single narrative. mpMRI-TRUS-guided fusion biopsy represents a major advancement in the prostate cancer diagnostic pathway, promoting precision oncology by reducing overtreatment and facilitating individualized patient care. This review aims to assist clinicians in adopting biopsy innovations that enhance diagnostic accuracy and improve patient outcomes. Full article

Background/Objectives: Prostate cancer is the second most common malignancy in men, and robot-assisted radical prostatectomy (RARP) has become a preferred treatment for localized disease. Postoperative urinary continence is a key determinant of quality of life. The aim of this study was to evaluate the preoperative patient characteristics and multiparametric magnetic resonance imaging (mpMRI) data that determine early postoperative continence in patients who underwent robotic radical prostatectomy at our clinic. Methods: In this study, patients who underwent robotic radical prostatectomy at our clinic between March 2020 and June 2022 were evaluated. The patients’ demographic data, preoperative PSA levels, digital rectal examination findings, preoperative lower urinary tract symptoms, sexual function, mpMRI findings, Briganti scores, surgical techniques used during the procedure and postoperative continence status were assessed. Results: A total of 111 patients were included in the study. The mean age of the patients was 61.1 years. The median follow-up duration was twelve months. According to the postoperative continence status, 22% of the patients were incontinent, 53% had moderate continence and 24% were fully continent in the first month. At the third month, 16.8% of the patients were incontinent, 31.3% had moderate continence and 51.8% were fully continent. At the one-year postoperative follow-up, the percentages of incontinent, moderately continent and fully continent patients were 4.8%, 13.2% and 81.9%, respectively. Urethral width in mpMRI (p: 0.012), pelvic transverse (p: 0.002) and AP (anterior–posterior) diameters (p: 0.033), preoperative IPSS scores (p: 0.033) and the presence of bilateral nerve-sparing surgery (p: 0.047) were found to be associated with postoperative urinary continence. No significant differences were found between groups regarding the relationship of other parameters evaluated by mpMRI with continence. Conclusions: In our study, preoperative IPSS scores, urethral width in mpMRI, pelvic transverse and AP diameters and the performance of nerve-sparing surgery were associated with early postoperative continence. Further studies with larger patient populations are needed to better understand the long-term predictors of postoperative urinary incontinence following radical prostatectomy. Full article

Background: Prostate cancer (PCa) represents a matter at the forefront of healthcare, being divided into clinically significant (csPCa) and indolent PCa based on prognostic and treatment options. Although multi-parametric magnetic resonance imaging (mpMRI) has enabled significant advances, it cannot differentiate between the aforementioned categories; therefore, in order to render the initial diagnosis, invasive procedures such as transrectal prostate biopsy are still necessary. In response to these challenges, artificial intelligence (AI)-based algorithms combined with radiomics features offer the possibility of creating a textural pixel pattern-based surrogate, which has the potential of correlating the medical imagery with the pathological report in a one-to-one manner. Objective: The aim of the present study was to develop a machine learning model that can differentiate indolent from csPCa lesions, as well as individually classifying each nodule into corresponding ISUP grades prior to prostate biopsy, using textural features derived from mpMRI T2WI acquisitions. Materials and Methods: The study was conducted in 154 patients and 201 individual prostatic lesions. All cases were scanned using the same 1.5 Tesla mpMRI machine, employing a standard protocol. Each nodule was manually delineated using the 3D Slicer platform (version 5.2.2) and textural parameters were derived using the PyRadiomics database (version 3.1.0). We compared three machine learning classification models (Random Forest, Support Vector Machine, and Logistic Regression) in full, partial and no correlation settings, in order to differentiate between indolent and csPCa, as well as between ISUP 2 and ISUP 3 lesions. Results: The median age was 65 years (IQR: 61–69), the mean PSA value was 10.27 ng/mL, and 76.61% of the segmented lesions had a PI-RADS score of 4 or higher. Overall, the highest performance was registered for the Random Forest model in the partial correlation setting, differentiating between indolent and csPCa and between ISUP 2 versus ISUP 3 lesions, with accuracies of 88.13% and 82.5%, respectively. When the models were trained on combined clinical data and radiomic signatures, these accuracies increased to 91.11% and 91.39%, respectively. Conclusions: We developed a machine learning decision support tool that accurately predicts the ISUP grade prior to prostate biopsy, based on the textural features extracted from T2 MRI acquisitions. Full article

Background/Objectives: The diagnostic approach to prostate cancer (PCa) has evolved from systematic biopsies to imaging-guided strategies that improve detection of clinically significant PCa (csPCa) while reducing overdiagnosis. Multiparametric magnetic resonance imaging (mpMRI) has emerged as the gold standard for pre-biopsy evaluation, while micro-ultrasound (MicroUS) offers a promising alternative with real-time imaging capabilities. Methods: We examined the principles, image interpretation frameworks (Prostate Imaging Reporting and Data System (PI-RADS) and Prostate Risk Identification using Micro UltraSound (PRI-MUS)), and clinical applications of mpMRI and MicroUS, comparing their diagnostic accuracy in biopsy-naïve patients, repeat biopsy scenarios, active surveillance, and staging. Results: mpMRI improves csPCa detection, reduces unnecessary biopsies, and enhances risk stratification. Landmark studies such as PRECISION (Prostate Evaluation for Clinically Important Disease: Sampling Using Image Guidance or Not?) and PRIME (Prostate Imaging Using MRI±Contrast Enhancement) confirm its superiority over systematic biopsy. However, mpMRI remains resource-intensive, with limitations in accessibility and interpretation variability. Conversely, MicroUS, with its high-resolution real-time imaging, shows non-inferiority to mpMRI and potential advantages in magnetic resonance imaging (MRI)-ineligible patients. It improves lesion visualization and biopsy targeting, with ongoing trials such as OPTIMUM (Optimization of prostate biopsy—Micro-Ultrasound versus MRI) evaluating its standalone efficacy. Conclusions: mpMRI and MicroUS are complementary modalities in PCa diagnosis. While mpMRI remains the preferred imaging standard, MicroUS offers an alternative, particularly in patients with MRI contraindications. Combining these techniques could enhance diagnostic accuracy, reduce unnecessary interventions, and refine active surveillance strategies. Future research should focus on integrating both modalities into standardized diagnostic pathways for a more individualized approach. Full article

While multi-parametric magnetic resonance imaging (mpMRI) is known to be a specific and reliable modality for the diagnosis of non-metastatic prostate cancer (PC), positron emission tomography (PET) using ⁶⁸Ga labeled ligands targeting the prostate-specific membrane antigen (PSMA) is known for its reliable detection of prostate cancer, being the most sensitive modality for the assessment of the extra-prostatic extension of the disease and the establishment of a diagnosis, even before biopsy. Background/Objectives: Here, we compared these modalities in regards to the localization of intraprostatic cancer lesions prior to local HDR brachytherapy. Methods: A cohort of 27 patients received both mpMRI and PSMA-PET/CT. Based on 24 intraprostatic segments, two readers each scored the risk of tumor-like alteration in each imaging modality. The detectability was evaluated using receiver operating characteristic (ROC) analysis. The histopathological findings from biopsy were used as the gold standard in each segment. In addition, we applied a patient-based “congruence” concept to quantify the interobserver and intermodality agreement. Results: For the ROC analysis, we included 447 segments (19 patients), with their respective histological references. The two readers of the MRI reached an AUC of 0.770 and 0.781, respectively, with no significant difference (p = 0.75). The PET/CT readers reached an AUC of 0.684 and 0.608, respectively, with a significant difference (p < 0.001). The segment-wise intermodality comparison showed a significant superiority of MRI (AUC = 0.815) compared to PET/CT (AUC = 0.690) (p = 0.006). Via a patient-based analysis, a superiority of MRI in terms of relative agreement with the biopsy result was observed (n = 19 patients). We found congruence scores of 83% (MRI) and 76% (PET/CT, p = 0.034), respectively. Using an adjusted “near total agreement” score (adjacent segments with positive scores of 4 or 5 counted as congruent), we found an increase in the agreement, with a score of 96.5% for MRI and 92.7% for PET/CT, with significant difference (p = 0.024). Conclusions: This study suggests that in a small collective of low-/intermediate risk prostate cancer, mpMRI is superior for the detection of intraprostatic lesions as compared to PSMA-PET/CT. We also found a higher relative agreement between MRI and biopsy as compared to that for PET/CT. However, further studies including a larger number of patients and readers are necessary to draw solid conclusions. Full article

Emerging imaging-guided technologies, such as prostate-specific membrane antigen radioguided surgery (PSMA-RGS) and augmented reality (AR), could enhance the precision and efficacy of robot-assisted prostate cancer (PCa) surgical approaches, maximizing the surgeons’ ability to remove all cancer sites and thus patients’ outcomes. Sentinel node biopsy (SNB) represents an imaging-guided technique that could enhance nodal staging accuracy by leveraging lymphatic mapping with tracers. PSMA-RGS uses radiolabeled tracers with the aim to improve intraoperative lymph node metastases (LNMs) detection. Several studies demonstrated its feasibility and safety, with promising accuracy in nodal staging during robot-assisted radical prostatectomy (RARP) and in recurrence setting during salvage lymph node dissection (sLND) in patients who experience biochemical recurrence (BCR) after primary treatment and have positive PSMA positron emission tomography (PET). Near-infrared PSMA tracers, such as OTL78 and IS-002, have shown potential in intraoperative fluorescence-guided surgery, improving positive surgical margins (PSMs) and LNMs identification. Finally, augmented reality (AR), which integrates preoperative imaging (e.g., multiparametric magnetic resonance imaging [mpMRI] of the prostate and computed tomography [CT]) onto the surgical field, can provide a real-time visualization of anatomical structures through the creation of three-dimensional (3D) models. These technologies may assist surgeons during intraoperative procedures, thus optimizing the balance between oncological control and functional outcomes. However, challenges remain in standardizing these tools and assessing their impact on long-term PCa control. Overall, these advancements represent a paradigm shift toward personalized and precise surgical approaches, emphasizing the integration of innovative strategies to improve outcomes of PCa patients. Full article

Focal Therapy (FT) is an emerging treatment modality for prostate cancer (PCa). Due to its novelty, the research exploring how patients should be followed-up after treatment is limited. There is currently no established role for non-prostate-specific-antigen (PSA) biomarkers and PSMA PET. However, a combination of PSA testing, multiparametric magnetic resonance imaging (mpMRI), and systematic and targeted biopsies should routinely be used for surveillance after FT. PSA values that rise 1.0 ng/mL over the nadir after twelve months or rise 1.5 ng/mL over nadir after twenty-four to thirty-six months should raise suspicion for recurrence. The standard imaging technique is mpMRI, but it can often be difficult to interpret after FT, so using a scoring system such as prostate imaging after focal ablation (PI-FAB) or the transatlantic recommendations for prostate gland evaluation with magnetic resonance imaging after focal therapy (TARGET) allows for greater consistency between readers. This review seeks to summarize the current literature regarding surveillance after FT as it relates to biomarkers, imaging, biopsies, and consensus statements. Full article

Background: Prostate cancer (PCa) diagnosis remains a complex field of study. Multiparametric magnetic resonance imaging (mpMRI) technology presents opportunities to enhance diagnostic precision. While recent advances in imaging and biopsy techniques show promise, the oncological implications of prebiopsy magnetic resonance imaging (MRI) and combination biopsy (ComBx) are not fully understood. This retrospective study evaluates the potential clinical impact of prebiopsy MRI and ComBx on PCa treatment outcomes. Methods: We conducted a comprehensive review of treatment-naïve patients undergoing prostate biopsy and subsequent radiation therapy (RT) or radical prostatectomy at the University of Cincinnati Health Center (2014–2020). Patients were categorized into two cohorts: those with prebiopsy mpMRI and ComBx versus those with systematic biopsy (SBx) alone. Patients with prostate-specific antigen (PSA) > 20 ng/mL were excluded. Biochemical recurrence (BCR) was defined as PSA ≥ 0.2 ng/mL post-prostatectomy or ≥2 ng/mL above nadir post-RT. Results: This study included 518 patients (189 SBx, 329 ComBx) with a median follow-up of 19.1 months. Median patient ages were 65.9 years (SBx) and 64.6 years (ComBx). The overall BCR rate was 10% with significantly lower rates in the ComBx group compared to SBx (6.4% vs. 16.4%, p < 0.001). Multivariable Cox regression analysis showed patients undergoing prebiopsy mpMRI with ComBx were 63% less likely to experience BCR (HR: 0.37, 95%CI 0.20–0.70, p = 0.002). Conclusions: Prebiopsy MRI followed by ComBx demonstrated lower BCR rates, suggesting improved PCa diagnosis and risk stratification. These findings highlight the potential of advanced imaging and biopsy techniques to benefit the management of PCa. Further longitudinal studies are needed to confirm the long-term clinical benefits of this approach. Full article

Background and Objectives: The Vesical Imaging–Reporting and Data System (VI-RADS) represents a standardized approach for interpreting multiparametric magnetic resonance imaging (mp-MRI) in bladder cancer (BC) evaluation. This systematic review aimed to assess the VI-RADS’ diagnostic performance and interobserver agreement in distinguishing muscle-invasive from non-muscle-invasive BC, a crucial differentiation for treatment planning. Materials and Methods: A systematic literature search was conducted through PubMed, Google Scholar, and Web of Science, over an initial five-year time span, from VI-RADS’ inception (May 2018) to November 2023. Studies reporting VI-RADS’ diagnostic performance with histopathological confirmation and interobserver agreement data were included. The diagnostic accuracy was assessed using sensitivity and specificity, while interobserver agreement was evaluated using Cohen’s κ coefficient. Results: Nine studies comprising 1249 participants met the inclusion criteria. Using a VI-RADS score cutoff of ≥3, the pooled sensitivity and specificity for detecting muscle invasion were 88.2% and 80.6%, respectively. Interobserver agreement showed excellent consistency with a mean κ value of 0.82. Individual study sensitivities ranged from 74.1% to 94.6%, while specificities varied from 43.9% to 96.5%. Conclusions: VI-RADS demonstrates high diagnostic accuracy and excellent interobserver agreement in BC staging, supporting its role as a reliable non-invasive diagnostic tool. However, it should be used as a complementary tool to, not a replacement for, histopathological confirmation. Moreover, the variability in specificity suggests the need for standardized training and interpretation protocols. Clinical correlation and adequate reader experience are essential for optimal implementation. Future integration with pathological data may further enhance its predictive value. Full article

Background: The increase in multiparametric magnetic resonance imaging (mpMRI) examinations as a fundamental tool in prostate cancer (PCa) diagnostics raises the need for supportive computer-aided imaging analysis. Therefore, we evaluated the performance of a commercially available AI-based algorithm for prostate cancer detection and classification in a multi-center setting. Methods: Representative patients with 3T mpMRI between 2017 and 2022 at three different university hospitals were selected. Exams were read according to the PI-RADSv2.1 protocol and then assessed by an AI algorithm. Diagnostic accuracy for PCa of both human and AI readings were calculated using MR-guided ultrasound fusion biopsy as the gold standard. Results: Analysis of 91 patients resulted in 138 target lesions. Median patient age was 67 years (range: 49–82), median PSA at the time of the MRI exam was 8.4 ng/mL (range: 1.47–73.7). Sensitivity and specificity for clinically significant prostate cancer (csPCa, defined as ISUP ≥ 2) were 92%/64% for radiologists vs. 91%/57% for AI detection on patient level and 90%/70% vs. 81%/78% on lesion level, respectively (cut-off PI-RADS ≥ 4). Two cases of csPCa were missed by the AI on patient-level, resulting in a negative predictive value (NPV) of 0.88 at a cut-off of PI-RADS ≥ 3. Conclusions: AI-augmented lesion detection and scoring proved to be a robust tool in a multi-center setting with sensitivity comparable to the radiologists, even outperforming human reader specificity on both patient and lesion levels at a threshold of PI-RADS ≥3 and a threshold of PI-RADS ≥ 4 on lesion level. In anticipation of refinements of the algorithm and upon further validation, AI-detection could be implemented in the clinical workflow prior to human reading to exclude PCa, thereby drastically improving reading efficiency. Full article

Background/Objectives: To analyze the predictive value of the apparent diffusion coefficient (ADC) in patients with prostate cancer (PCa) treated with radiotherapy (RT) and androgen deprivation therapy (ADT). Methods: Retrospective study of patients with high-risk, very high-risk, or unfavorable intermediate-risk PCa who received RT and ADT between 2008 and 2019 and underwent multiparametric magnetic resonance imaging mpMRI) at 6 months post-RT. Differences in ADC values were compared between patients with and without progression and/or local recurrence. Receiver operating characteristic (ROC) curves were used to obtain ADC cutoffs for predicting 10-year progression-free-survival (PFS) and local recurrence-free survival (LRFS). Results: We evaluated 98 patients (73 [74.5%] high-risk). Over a mean ± SD follow-up of 95.36 ± 30.54 months, 19 patients (19.4%) progressed; at 10 years, PFS was 75.6%, LRFS 93.8%, metastasis-free survival 85.5%, and overall survival 89.5%. Post-RT ADC was significantly lower in patients with local recurrence (1.09 ± 0.18 vs. 1.30 ± 0.20 × 10⁻³ mm²/s, p = 0.020) and progression (1.23 ± 0.20 vs. 1.30 ± 0.21 × 10⁻³ mm²/s, p = 0.004). ROC analysis identified a post-RT ADC cutoff of 1.11 × 10⁻³ mm²/s for local recurrence (area under curve [AUC] 0.843, sensitivity 89.4%, positive predictive value [PPV] 98.8%). The cutoff for progression was 1.24 × 10⁻³ mm²/s (AUC0.705, sensitivity 72.2%, PPV87.7%). Patients with a post-RT ADC value below and above 1.11 × 10⁻³ mm²/shad a 10-year LRFS of 66.8% and 97.7%, respectively (HR: 25.04 [2.58–242.92], p < 0.001). The corresponding rates for 10-year PFS were 58.6% and 85.6% in patients with post-RT ADC values below and above 1.24 × 10⁻³ mm²/s (HR: 2.916 [1.113–7.644], p = 0.015). In the multivariate analysis, a post-treatment ADC value ≤ 1.24 × 10⁻³ mm²/s was a significant prognostic factor for a lower PFS (HR: 3823 [1371–10,657], p = 0.010). Conclusions: This is the first study to show that post-RT ADC can be a predictive factor of local recurrence in PCa treated with RT and ADT. Moreover, this long-term study demonstrates its value as a predictive factor of progression in PCa treated with RT and ADT. Full article

Background/Objectives: Multiparametric-Magnetic Resonance Imaging(mp-MRI) presents the ability to detect clinically significant cancer, aiming to avoid biopsy if the results are negative or target an abnormal lesion if a suspected lesion of the prostate is found. Recent guidelines recommend the performance of 12 standard biopsies along with 3 to 5 targeted biopsies in suspected prostate lesions, depending on the size of the prostate lesion. In addition, prostate biopsy can be performed by either the transperineal or the transrectal approach. The aim of this comprehensive review is to highlight the role of both standard and targeted MRI/Ultrasound (US) fusion transperineal biopsy (TPB) in the diagnostic approach of prostate cancer cases, to report its diagnostic efficacy and complication rates and to suggest the promising usage of MRI/US fusion TPB in the future. Methods: A comprehensive review of the existing literature, including systematic reviews, meta-analyses, and clinical guidelines, was conducted to compare the efficacy and safety of transperineal and transrectal approaches in prostate cancer detection. Special emphasis was placed on mp-MRI-guided targeted biopsy and its combination with systematic sampling. Results: Prostate biopsy via the transperineal approach is related to increased detection rates, especially for anterior lesions, and decreased infection risk compared to the transrectal approach, while complication rates (hematuria, hemospermia, etc.) remain similar. Due to lower infection rates via the transperineal route, the performance of prostate biopsy using the transperineal approach is strongly recommended. Finally, transperineal fusion MRI/US biopsy can be valuable for repeat biopsies in patients who had an initial negative biopsy or for the follow-up of patients that undergo active surveillance. Conclusions: MRI/US fusion-guided TPB represents a significant advancement in prostate cancer diagnostics, combining improved precision with reduced infection risks. Although TPB presents higher detection rates for anterior prostatic lesions and lower post-biopsy infection rates, there is no significant difference in cancer detection rates compared to TRB. Targeted training and investment may reduce long-term expenses of TPB by lowering hospitalizations, antibiotic usage, and related costs. Future research should further refine this approach and explore its integration with emerging technologies like artificial intelligence for enhanced lesion targeting and diagnostic accuracy. Full article

Background: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have a one-to-one correspondence with the histopathological grading of tumors. Recently, artificial intelligence (AI)-based algorithms and textural analysis, a subdivision of radiomics, have shown potential in bridging this gap. Objectives: We aimed to develop a machine-learning algorithm that predicts the ISUP grade of manually contoured prostate nodules on T2-weighted images and classifies them into clinically significant and indolent ones. Materials and Methods: We included 55 patients with 76 lesions. All patients were examined on the same 1.5 Tesla mpMRI scanner. Each nodule was manually segmented using the open-source 3D Slicer platform, and textural features were extracted using the PyRadiomics (version 3.0.1) library. The software was based on machine-learning classifiers. The accuracy was calculated based on precision, recall, and F1 scores. Results: The median age of the study group was 64 years (IQR 61–68), and the mean PSA value was 11.14 ng/mL. A total of 85.52% of the nodules were graded PI-RADS 4 or higher. Overall, the algorithm classified indolent and clinically significant PCas with an accuracy of 87.2%. Further, when trained to differentiate each ISUP group, the accuracy was 80.3%. Conclusions: We developed an AI-based decision-support system that accurately differentiates between the two PCa prognostic groups using only T2 MRI acquisitions by employing radiomics with a robust machine-learning architecture. Full article

Background: To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. Methods: The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected. Patients with benign prostate pathology were classified as Group 1, and those with prostate cancer (PCa) were classified as Group 2. The following ML algorithms were used to create the ML model: ExtraTrees classifier, Light Gradient-Boosting Machine (LGBM) classifier, eXtreme Gradient Boosting (XGB) classifier, Logistic Regression, and Random Forest classifier. Results: A total of 244 male patients who met the inclusion criteria were included in this study. Among them, 171 (71.1%) were categorized in Group 1, and 73 (29.9%) in Group 2. The LGBM classifier model demonstrated the highest performance, achieving an accuracy rate of 81.6% and an AUC–ROC (area under the curve–receiver operating characteristic) of 78.4%, with sensitivity and specificity values of 66.7% and 88.2%, respectively, in predicting prostate biopsy outcomes. Conclusions: Pathological results can be predicted by ML models using PSA values, mpMRI findings, and hematologic parameters prior to a prostate biopsy, potentially reducing unnecessary biopsy procedures. Full article

Background: 5-alpha reductase inhibitors (5-ARIs) change hormonal pathways and reduce prostate size. We evaluated the effects of 5-ARIs on prostatic multiparametric magnetic resonance imaging (mpMRI) suspicious findings and in the identification of prostate cancer using targeted biopsies. Methods: We conducted a retrospective study including 600 consecutive patients who, between 2017 and 2021, underwent combined transperineal fusion biopsies. Primary outcomes were Prostate Imaging Reporting and Data System version 2 (PIRADS v2) scores and the identification of clinically significant prostate cancer from suspicious lesions (targeted CSPC). Outcomes were compared between patients treated with 5-ARIs for a minimum of 6 months and the other patients. Results: Patients treated with 5-ARIs were older (p < 0.001) with higher rates of previous prostate biopsies (p = 0.004). PIRADS scores were 3, 4, and 5 in 15 (29%), 28 (54%), and 9 (17%) patients among the 5-ARI group and 130 (24%), 308 (56%), and 110 (20%) patients among the others, and the scores were not different between the groups (p = 0.69). The targeted CSPC identification rate among 5-ARI patients was 31%, not different compared to the non-5-ARI group (p = 1). Rates of targeted CSPC for each PIRADS score were not affected by 5-ARI treatment. The 5-ARI was not associated with neither PIRADS ≥ 4 score nor targeted CSPC on logistic regression analyses (OR = 0.76, 95% CI 0.4–1.4 and OR = 1.02, 95% CI 0.5–1.9, respectively). Conclusions: 5-ARI treatment is not associated with PIRADS score alterations or targeted biopsy results. Patients treated by 5-ARIs with suspicious lesions should not be addressed differently during the mpMRI-related diagnostic process. Full article