Search Results (59)

Background: prostate fusion biopsies are key in the diagnosis of prostate cancer (PCa); however, the fusion imaging system is not always user-friendly or reliable. The aim of this study was to assess the feasibility, accuracy, and effectiveness of transperineal fusion biopsies performed with a novel fusion imaging device equipped with AI-driven auto-contouring. Methods: data from 148 patients who underwent MRI-targeted and systematic prostate fusion biopsy with UroFusion (Esaote) were prospectively collected. All biopsies were performed in-office, under local anaesthesia. Results: cancer detection rate was 64% overall and 56% for clinically significant PCa (csPCa, ISUP ≥ 2). PCa was detected in 35%, 65% and 84% of lesions scored as PI-RADS 3, 4 and 5, respectively. Outfield positive systematic cores were found in the contralateral lobe in one third of cases. Median device-time to obtain fusion imaging was 5 min and median biopsy duration was 15 min. Median difference in volume estimation between ultrasound and MRI auto-contouring was only 1 cc. Detection rate did not differ between experienced and novice, supervised users. Conclusions: in this initial prospective experience, fusion biopsies performed with UroFusion AI-driven auto-contouring system appeared time-efficient, accurate, well tolerated, and user-friendly, with comparable outcomes between experienced and novice users. Systematic biopsies remain highly recommended given the non-negligible rates of positive outfield cores. Full article

Background: Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. Methods: We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI. Results: Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%. Conclusions: Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions. Full article

Background/Objectives: It remains unclear whether certain areas of the prostate are more difficult to accurately sample using MRI/US-fusion-guided freehand single-access transperineal prostate biopsy (FSA-TP). The aim of this study was to evaluate the detection rates of clinically significant (cs) and clinically insignificant (ci) prostate cancer (PCa) in each prostate zone during FSA-TP MRI-target biopsies (MRI-TBs) and systematic biopsies (SB). Methods: This monocentric observational study included a cohort of 277 patients with no prior history of PCa who underwent 3 MRI-TB cores and 14 SB cores with an FSA-TP from January to December 2023. The intraclass correlation coefficient (ICC) was assessed to evaluate the correlation between the Prostate Imaging–Reporting and Data System (PI-RADS) of the index lesion and the International Society of Urological Pathology (ISUP) grade stratified according to prostate zone and region of index lesion at MRI. Multivariate logistic regression analysis was conducted to identify factors associated with PCa and csPCa in patients with discordant results between MRI-TB and SB. Results: FSA-TP-MRI-TB demonstrated higher detection rates of both ciPCa and csPCa in the anterior, apical, and intermediate zones when each of the three MRI-TB cores was analysed separately (p < 0.01). However, when all MRI-TB cores were combined, no significant differences were observed in detection rates across prostate zones (apex, mid, base; p = 0.57) or regions (anterior vs. posterior; p = 0.34). Concordance between radiologic and histopathologic findings, as measured by the intraclass correlation coefficient (ICC), was similar across all zones (apex ICC: 0.33; mid ICC: 0.34; base ICC: 0.38) and regions (anterior ICC: 0.42; posterior ICC: 0.26). Univariate analysis showed that in patients with PCa detected on SB but with negative MRI-TB, older age was the only significant predictor (p = 0.04). Multivariate analysis revealed that patients with PCa detected on MRI-TB but with negative SB, only PSA remained a significant predictor (OR 1.2, 95% CI 1.1–1.4; p = 0.01). In cases with csPCa detected on MRI-TB but with negative SB, age (OR: 1.0, 95% CI 1.0–1.1; p = 0.02), positive digital rectal examination (OR: 2.0, 95% CI 1.1–3.8; p = 0.03), PI-RADS score >3 (OR: 4.5, 95% CI 1.7–12.1; p < 0.01), and larger lesion size (OR: 1.1, 95% CI 1.1–1.2; p < 0.01) were significant predictors. Conclusions: FSA-TP using 14 SB cores and 3 MRI-TB cores ensures comprehensive sampling of all prostate regions, including anterior and apical zones, without significant differences in detection rates between nodules across different zones. Only in a small percentage of patients was csPCa detected exclusively by SB, highlighting the small but important complementary value of combining SB and MRI-TB. 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: Prostate cancer is the second most prevalent malignancy in men globally, typically suspected following abnormalities found during digital rectal examination (DRE) or elevated PSA levels. This study aimed to evaluate and compare the effectiveness of two biopsy techniques—TRUS-Bx and ComBx—in detecting prostate cancer, particularly focusing on the identification of clinically significant prostate cancer (csPCa) with an International Society of Urological Pathology (ISUP) grade ≥ 2. Methods: This retrospective cohort study involved 500 men (aged 46 to 79, with an average age of 65) who had prostate biopsies at our institution between 2017 and 2022. The patients were divided into two groups: 250 men received a transrectal US-guided biopsy (TRUS-Bx) with a standard 12-core sampling, while the other 250 underwent a transperineal combined fusion biopsy (ComBx) with MRI guidance. The ComBx group targeted areas classified as PI-RADS ≥ 3 and also included additional systematic samples. Tumor detection rates for both techniques were assessed, with a particular focus on overall PCa detection and the identification of csPCa. Results: In the TRUS-Bx group, the mean PSA level was 8.2 ng/mL (1.8–45.2 ng/mL), and in the ComBx group, the mean PSA level was 7.5 ng/mL (0.8–32.4 ng/mL). ComBx demonstrated superior detection rates for PCa compared to TRUS-Bx, with statistically significant differences observed in the overall detection of PCa with ISUP grade ≥ 1 (61% for ComBx vs. 45% for TRUS-Bx; p < 0.001) and csPCa (40% for ComBx vs. 30% for TRUS-Bx; p = 0.019). In the ComBx group for csPCa, the detection rates in targeted biopsies of MRI-identified lesions assessed as PIRADS 3, 4, and 5 were 17%, 51%, and 78%, respectively. Conclusions: ComBx offers significantly improved efficacy in detecting prostate cancer, particularly in identifying clinically significant cases, compared to systematic TRUS-Bx. These findings support ComBx as a valuable tool in enhancing diagnostic accuracy for csPCa. Full article

Background/Objectives: The objective of this study was to evaluate the oncological outcomes and safety of nerve-sparing (NS) robot-assisted radical prostatectomy (RARP) when applied without Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions or Gleason pattern ≥4 on biopsy in the peripheral zone (PZ). Methods: We retrospectively analyzed 208 patients who underwent RARP between August 2017 and December 2022, excluding those who had received preoperative hormonal therapy. After NS status stratification and patient characteristic adjustment using propensity score matching (PSM), positive resection margin (RM) rates and prostate-specific antigen (PSA) recurrence-free survival were compared. Urinary and sexual quality of life (QOL) were assessed using the Expanded Prostate Cancer Index Composite, along with predictive factors associated with positive RM and RM locations in the NS group. Results: NS was performed in 68.6% (n = 129) patients. After PSM, there were no significant differences in RM positivity (p = 0.811) or PSA recurrence-free survival (Log-rank p = 0.79), regardless of NS status. There was no difference in sexual function between groups, but urinary QOL was significantly better in the NS group from the third month onward. In the NS group, RM positivity was 27.9% (n = 36), and diagnostic PSA (odds ratio [OR], 1.110, p = 0.038) and clinical T stage (OR, 1.400, p = 0.038) were predictive factors. The RM positivity rate on the NS side was 10.8%. Conclusions: NS, based on the absence of PI-RADS ≥3 lesions or Gleason pattern ≥4 in PZ, did not increase RM positivity rate and increased early urinary QOL. 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: Evidence to help avoid unnecessary prostate biopsies is being actively pursued. Our goal was to develop and internally validate a nomogram for predicting clinically significant prostate cancer (csPC) in men with low suspicion of disease (prostate specific antigen [PSA] < 10 ng/mL, normal digital rectal examination [DRE]), in whom magnetic resonance imaging (MRI) findings are positive. Methods: Patients with no prior prostate cancer diagnosis who underwent MRI–ultrasound fusion biopsy of the prostate were retrospectively analyzed. Inclusion criteria were PSA < 10 ng/mL, normal DRE, Prostate Imaging Reporting And Data System (PIRADS) category ≥ 3, and no extraprostatic extension or seminal vesicle invasion reported on MRI. Associations between csPC diagnosis and patient or lesion characteristics were analyzed, and a multivariable model was developed. Internal validation of the model with 5-fold cross-validation and bootstrapping methods was performed. Results: Among 209 patients, 67 were diagnosed with csPC. Factors incorporated into the model for predicting csPC were age, 5-alpha reductase inhibitor use, PSA, prostate volume, PIRADS > 3, and lesion location in the peripheral zone. The model’s ROC AUC was 0.86, with consistent performance at internal validation (0.84 with cross-validation, 0.82 with bootstrapping). With an empirical threshold of <10% csPC probability to omit biopsy, 72 (50.7%) unnecessary biopsies would have been avoided, at the cost of missing 2 (3.0%) csPC cases. Conclusions: Our nomogram might serve as a valuable tool in refining selection criteria in men considered for prostate biopsy. The major limitation of the study is its retrospective character. Prospective, external validation of the model is warranted. Full article

Background. Inflammatory features can mimic PCa in suspicious MRI-lesions. Objectives: To assess the incidence of inflammatory features in targeted biopsies to suspicious lesions. Methods. A prospective analysis was conducted of 531 MRI-suspicious lesions with Prostate Imaging-Reporting and Data System (PI-RADS) scores of 3 to 5 in 364 men suspected of having PCa. Results. The incidence of inflammatory features in the MRI-suspicious lesions without PCa was 69.6%, compared to 48.1% in those with PCa (p < 0.001). Among the suspicious lesions without PCa, the incidence of inflammatory features ranged from 68.6% to 71.2% across the PI-RADS categories (p = 0.870). Mild chronic prostatitis increased with higher PI-RADS scores, while acute prostatitis decreased, and granulomatous prostatitis was exclusively observed in patients with PI-RADS scores of 4 and 5. The incidence of inflammatory features in the lesions with insignificant PCa (grade group 1) was 66.7%, compared to 42.7% in those with significant PCa (grade group 2 to 5; p = 0.027). The detection of inflammatory features in MRI-suspicious lesions was identified as an independent predictor of a lower likelihood of significant PCa detection, with an odds ratio (OR) of 0.326 (95% CI 0.196–0.541). Mild chronic prostatitis was the only type of prostatitis which was an independent predictor of a lower likelihood of significant PCa, with an OR of 0.398 (95% CI 0.268–0.590). Conclusions. These data suggest that inflammatory features may be considered mimickers of significant PCa on MRI. 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

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs >0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions. Full article

Prostate cancer is the second most common cancer in men and a leading cause of death worldwide. Early detection is vital, as it often presents with vague symptoms such as nocturia and poor urinary stream. Diagnostic tools like PSA tests, ultrasound, PET-CT, and mpMRI are essential for prostate cancer management. The PI-RADS system helps assess malignancy risk based on imaging. While mpMRI, which includes T1, T2, DWI, and dynamic contrast-enhanced imaging (DCE), is the standard, bpMRI offers a contrast-free alternative using only T2 and DWI. This reduces costs, acquisition time, and the risk of contrast-related side effects but has limitations in detecting higher-risk PI-RADS 3 and 4 lesions. This study compared bpMRI’s diagnostic accuracy to mpMRI, focusing on prostate volume and PI-RADS scoring. Both methods showed strong inter-rater agreement for prostate volume (ICC 0.9963), confirming bpMRI’s reliability in this aspect. However, mpMRI detected more complex conditions, such as periprostatic fat infiltration and iliac lymphadenopathy, which bpMRI missed. While bpMRI offers advantages like reduced cost and no contrast use, it is less effective for higher-risk lesions, making mpMRI more comprehensive. Full article

Background: Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. Method: A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. Results: All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. Conclusions: The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa. Full article

Background: MRI fusion prostate biopsy has improved the detection of clinically significant prostate cancer (CSC). Continued refinements in predicting the pre-biopsy probability of CSC are essential for optimal patient counseling. We investigated potential factors related to improved cancer detection rates (CDR) of CSC in patients with PI-RADS ≥ 3 lesions. Methods: The pathology of 980 index lesions in 980 patients sampled by transrectal mpMRI-targeted prostate biopsy across four medical centers between 2017–2020 was reviewed. PI-RADS lesion distribution included 291 PI-RADS-5, 374 PI-RADS-4, and 315 PI-RADS-3. We compared CDR of index PI-RADS ≥ 3 lesions based on location (TZ) vs. (PZ), PSA density (PSAD), and history of prior negative conventional transrectal ultrasound-guided biopsy (TRUS). Results: Mean age, PSA, prostate volume, and level of prior negative TRUS biopsy were 66 years (43–90), 7.82 ng/dL (5.6–11.2), 54 cm³ (12–173), and 456/980 (46.5%), respectively. Higher PSAD, no prior history of negative TRUS biopsy, and PZ lesions were associated with higher CDR. Stratified CDR highlighted significant variance across subgroups. CDR for a PI-RADS-5 score, PZ lesion with PSAD ≥ 0.15, and prior negative biopsy was 77%. Conversely, the CDR rate for a PI-RADS-4 score, TZ lesion with PSAD < 0.15, and prior negative biopsy was significantly lower at 14%. Conclusions: For index PI-RADS ≥ 3 lesions, CDR varied significantly based on location, prior history of negative TRUS biopsy, and PSAD. Such considerations are critical when counseling on the merits and potential yield of prostate needle biopsy. Full article

(1) Background: To identify a particular setting of biopsy-naïve patients in which it would be reasonable to offer only cognitive targeted prostate biopsy (PBx) with a transrectal approach. (2) Methods: We designed an observational retrospective pilot study. Patients with a prostatic specific antigen (PSA) level > 10 ng/mL, either a normal or suspicious digital rectal examination (DRE), and a lesion with a PI-RADS score ≥ 4 in the postero-medial or postero-lateral peripheral zone were included. All patients underwent a transrectal PBx, including both systematic and targeted samples. The detection rate of clinically significant prostate cancer (csPCa) (Gleason Score ≥ 7) was chosen as the primary outcome. We described the detection rate of csPCa in systematic PBx, targeted PBx, and overall PBx. (3) A total of 92 patients were included. Prostate cancer was detected in 84 patients (91.30%) with combined biopsies. A csPCa was diagnosed in all positive cases (100%) with combined biopsies. Systematic PBxs were positive in 80 patients (86.96%), while targeted PBxs were positive in 84 men (91.30%). Targeted PBx alone would have allowed the diagnosis of csPCa in all positive cases; systematic PBx alone would have missed the diagnosis of 8/84 (9.52%) csPCa cases (4 negative patients and 4 not csPCa) (p = 0.011). (4) Conclusions: Cognitive targeted PBx with a transrectal approach could be offered alone to diagnose csPCa in biopsy-naïve patients with PSA ≥ 10 ng/mL, either normal or suspicious DRE, and a lesion with PI-RADS score ≥ 4 in the postero-medial or postero-lateral peripheral zone. Full article