Search Results (193)

Introduction: Clinical staging based on digital rectal examination is imprecise, leading to pathological upstaging in patients with prostate cancer (PCa). Accurate preoperative assessment remains a challenge despite the use of multiparametric magnetic resonance imaging (mpMRI) and fusion-guided biopsy. This study aims to identify key predictors of upstaging in preoperative patients. Materials and Methods: A retrospective analysis of 924 patients who underwent radical prostatectomy between July 2012 and January 2025 was performed. Variables included prostate-specific antigen, prostate volume, biopsy type, MRI, body mass index and age. Upstaging was defined as ≥pT3 in patients staged clinically as cT1–2. Optimal cut-offs for continuous variables were defined statistically. Multivariable logistic regression was applied to identify independent predictors of upstaging and minor staging upgrading (MSU)—defined as any upward shift in the pathological T stage relative to the clinical T stage. Model performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: Upstaging occurred in 31.9% and MSU in 50.6% of patients. The mean age was 65 years. Cut-off values for PSA density (PSAD) were 0.29 for upstaging and 0.28 for MSU. In the full-cohort model (AUC = 0.628), PSAD (odds ratio (OR) = 2.55), age (OR = 1.04), and hypertension (HT) (OR = 1.47) were associated with upstaging. In PIRADS-based models, PIRADS 5 and PSAD predicted both upstaging (OR = 1.62 and 6.10, respectively; AUC = 0.664) and MSU (OR = 1.75 and 4.67, respectively; AUC = 0.659). MSU was also associated with HT and a lack of fusion biopsy (AUC = 0.622). Conclusions: PSAD and PIRADS 5 lesions are strong determinants of pathological upstaging and MSU in PCa. These factors should be considered in preoperative risk stratification to improve staging accuracy. Despite advances in imaging and biopsy techniques, upstaging remains a common phenomenon, underlining the need for further refinement of diagnostic protocols. Full article

Background/Objectives: Prostate cancer (PCa) incidence is rising, with radical prostatectomy (RP) as the main curative surgery for localised cases, which includes removing seminal vesicles (SV). SV invasion (SVI) predicts poor oncological outcomes, making accurate preoperative staging to identify SVI crucial for surgical planning. This ensures oncological safety by enabling wide excision when needed, while preserving tissue to maintain function. This review synthesises current evidence on pre-biopsy MRI findings and/or clinicopathological parameters to diagnose SVI in PCa. Methods: A literature search (2005–2025) using OVID for studies assessing pre-biopsy MRI findings, with a priori eligibility for clinicopathological or combined MRI–clinicopathological models (index tests), for detecting SVI (outcome) compared to RP histopathology (standard reference) in patients with primary localised PCa (patients). This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Risk of bias was evaluated with QUADAS-2, and pooled diagnostic accuracy metrics and study heterogeneity were analysed. Results: Five studies qualified, while three used binary mpMRI classification and were quantitatively analysed. No eligible studies assessed clinicopathological predictors or combined MRI–clinicopathological models; all included studies evaluated pre-biopsy MRI findings only, and none included high-dimensional radiomics. The pooled sensitivity was 0.66 (95% CI: 0.52–0.78), specificity 0.94 (0.89–0.97), positive predictive value (PPV) 0.76 (0.60–0.87), negative predictive value (NPV) 0.92 (0.85–0.94), and diagnostic odds ratio 30.13 (12.36–73.47), with moderate heterogeneity. All included studies were retrospective cohorts with considerable risk of bias. Conclusions: In the small number of heterogeneous, single-centre retrospective studies available, pre-biopsy MRI findings show high specificity and NPV for preoperative detection of SVI but only moderate sensitivity, which limits its reliability as a standalone tool. The pooled diagnostic accuracy estimates should be interpreted as exploratory. These findings should therefore be interpreted cautiously. Future studies must integrate MRI with clinicopathological data, addressing this key evidence gap before firm conclusions can be drawn or clinical practice changed. Full article

Individual differences in second-language (L2) proficiency are expected to influence how listeners parse and represent continuous speech, yet their neural signatures under naturalistic conditions remain unclear. We investigated this question using task-based fMRI during continuous speech listening. A total of 43 healthy participants completed four listening runs synchronized with MRI acquisition via PsychoPy(Peirce 2007), with eyes open throughout scanning. To promote sustained attention and comprehension, participants provided a native-language oral recall after each run. Based on behavioral proficiency scores, participants were grouped into low- (LP, n = 14), moderate- (MP, n = 14), and high-proficiency (HP, n = 15) groups. We evaluated three temporal information-encoding frameworks derived from BOLD dynamics: direct temporal series, functional connectivity (FC), and self-information weighted inter-subject correlation (ISC-W). Using a 10 × 5-fold nested cross-validation scheme, we tested both categorical classification (Support Vector Machines) for discrete proficiency groups (LP, MP, HP) and continuous multivariate regression (Ridge/Lasso) for continuous proficiency scores. Furthermore, we applied ROI-based ANOVA and univariate Neural Correlation Analysis (NCA) to identify key brain regions, evaluating significance via nonparametric permutation testing (1000 permutations) and False Discovery Rate (FDR) correction. Results indicated that while categorical classification yielded numerical trends—with ISC-W performing best—it did not reach statistical significance under stringent permutation testing. However, multivariate continuous regression using ISC-W features successfully predicted continuous proficiency scores with statistical significance (p < 0.05). Exploratory ROI analysis highlighted the bilateral orbital inferior frontal gyrus (IFG_orb_bilat) as a highly sensitive region. These findings suggest that L2 proficiency is best represented as a distributed, continuous neural variable, and that self-information weighting effectively filters background noise to capture cognitive variance. Methodologically, this study provides a reproducible pipeline integrating information-theoretic feature construction with rigorous whole-brain nonparametric inference. Full article

Background/Objectives: Active surveillance (AS) has emerged as a safe alternative to primary therapy for low- and select intermediate-risk prostate cancer (PCa), but optimal patient selection and surveillance strategies remain challenging due to limited risk stratification tools enabling early detection of lesions with high potential for histopathological progression. This study presents an integrated method for predicting prostate cancer progression within 12 months, aiming to improve AS patient selection by categorizing patients into two risk groups: rapid progressors who would benefit from immediate treatment and slow progressors suitable for AS. Methods: The risk assessment platform combines convolutional neural networks for automatic segmentation of prostate and suspicious-for-cancer lesions on multiparametric MRI (mpMRI) with logistic regression to estimate progression risk. The networks were trained on annotated lesions from radical prostatectomy specimen mapped to mpMRI. The prediction model incorporated pre-biopsy clinical variables (age, PSA, PI-RADS) and MRI-derived intratumoral radiomic features from 163 participants of a prospective clinical trial, using histopathological progression within 12 months as endpoint. Results: The clinical-radiomics model achieved an AUC of 0.84 in distinguishing rapid from slow progressors, using non-invasive monitoring techniques. In an independent test set, the model significantly improved AS patient selection, increasing negative predictive value by 18.5% compared to current standard-of-care (p < 0.001). Conclusions: The risk assessment platform shows promise for use during annual follow-up visits to reliably differentiate suitable AS candidates with stable disease from PCa patients who are likely to experience early progression. Full article

Background: The PROSPET-BX trial compared [⁶⁸Ga]PSMA-11 PET/CT (PSMA-PET) with multiparametric MRI (mpMRI) in parallel in men with suspicion of prostate cancer (PCa) after at least one previously negative biopsy (ClinicalTrials.gov: NCT05297162; GR-2018-12366240). In this study, we performed the cost analysis of the two imaging modalities with respect to the detection of clinically significant PCa (csPCa). Methods: We analyzed the data from patients enrolled in the trial who met the inclusion criteria. For the cost analysis, we identified six competing triage strategies, each defined as a binary decision rule for referral to prostate biopsy: (1) biopsy-all; (2) elevated PSA-density (PSAD; biopsy if PSAD > 0.15 ng/mL/cc; (3) mpMRI positive (PIRADS 3–5); (4) PSMA-PET positive (PRIMARY 3–5); (5) mpMRI or PSMA-PET positive; (6) PSAD and mpMRI. For each strategy, we yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for csPCa. Direct hospital costs were modeled from a provider perspective, incorporating testing and procedural costs. Unit costs (in EUR) were sourced from our institutional accounting records. Pairwise cost-effectiveness comparisons were performed using incremental cost-effectiveness ratio (ICER) and incremental net benefit (INB). Results: Among the six triage strategies evaluated, the “biopsy-all” approach achieved perfect sensitivity, whereas the PSAD + mpMRI pathway was the most parsimonious strategy but missed 14 csPCa cases (53.8%). The combined “mpMRI or PSMA-PET” strategy maximized detection (22 cPCa, missing only 4) at an intermediate cost (EUR 81.991 total; EUR 3.727 per csPCa). The pairwise comparison of each strategy with mpMRI alone showed for the mpMRI or PSMA-PET pathway a low ICER (~EUR 2.900/extra csPCa), with consistently positive and increasing INB across higher WTP (willingness-to-pay). Therefore, this combination provided the most favorable cost-effectiveness profile, balancing detection, efficiency, and cost. Conclusions: To the best of our knowledge, this is the first cost analysis study to compare different strategies incorporating PSMA-PET in the re-biopsy setting, demonstrating that the combined “mpMRI or PSMA-PET” pathway is the most cost-effective diagnostic pathway for csPCa detection. Full article

Background: Prostate cancer (PCa) shows a marked biological heterogeneity that is closely associated with tumor aggressiveness. A substantial proportion of clinically significant tumors remain undetected by multiparametric magnetic resonance imaging (mpMRI). Elucidating the molecular basis of MRI visibility and identifying non-invasive biomarkers could improve the risk stratification and clinical management of patients. Accordingly, this study aimed to assess tissue and urine proteomic signatures associated with PCa aggressiveness and mpMRI visibility. Methods: In this exploratory study, we performed an integrated proteomic analysis of prostate tissue and preoperative urine samples from 24 patients stratified into four groups: benign prostatic hyperplasia (BPH), indolent PCa (Gleason 6), clinically significant PCa with MRI-visible lesions, and clinically significant PCa with MRI-non-visible lesions. Data-independent acquisition mass spectrometry (DIA workflows) was used to identify differentially expressed proteins associated with malignancy, tumor aggressiveness, and MRI visibility. Results: Pairwise proteomic analyses revealed significant molecular differences between BPH and all PCa groups, identifying 694 non-redundant proteins differentially expressed in tissue and 482 in preoperative urine, showing molecular features associated with both disease presence and progression. Comparative tissue and urine analyses identified 82 proteins, reflecting shared biological pathways in metabolism, cytoskeletal organization, immune processes, and extracellular matrix remodeling. Finally, a direct comparison of MRI-visible and MRI-non-visible clinically significant PCa identified a panel of differentially expressed proteins, including LCN2/NGAL, S100A9, and AOC1/DAO, that showed differential urinary abundance and prognostic relevance in the TCGA-PRAD cohort. Conclusions: Our results suggest that proteomic alterations in PCa are associated with disease progression and aggressiveness and capture biologically relevant differences between tissue and urinary proteomes. These differences are also observed between MRI-visible and MRI-non-visible clinically significant prostate cancers, supporting the potential of urinary proteomics as a non-invasive complement to imaging-based diagnostics. Full article

Background/Objectives: Despite multiparametric MRI (mpMRI)-guided biopsy, clinically significant upgrading (CSU) of ISUP Grade Group (GG) at radical prostatectomy (RP) remains common in prostate cancer (PCa). We aimed to identify predictors of CSU (biopsy GG ≤ 2 to RP GG ≥ 3) using routine preoperative variables, and to benchmark a parsimonious logistic model against multiple machine learning (ML) classifiers. Methods: In this single-center exploratory analysis, 96 consecutive PCa patients underwent pre-biopsy mpMRI, systematic ± MRI-targeted biopsy, and RP. Predictive modeling was restricted to biopsy GG 1–2 patients (n = 64). LASSO-guided feature selection and Firth-penalized logistic regression were used to build a locked reference model, evaluated against ML classifiers using cross-validated discrimination, calibration, and decision curve analysis. Results: CSU occurred in 10/64 patients (15.6%). Positive core ratio was the dominant independent predictor (adjusted OR 1.54 per 10% increase, 95% CI 1.10–2.17). PSA density (PSAD) showed a consistent positive association but did not retain independent significance. The locked two-variable model (AUC ≈ 0.75–0.79) outperformed all ML classifiers in discrimination, calibration, and net clinical benefit; however, the limited event count (n = 10) constrains model stability, and these findings require external validation. Conclusions: In a PCa mpMRI-informed diagnostic pathway, CSU is primarily driven by biopsy tumor burden. A simple logistic model based on positive core ratio and PSAD outperformed more complex ML approaches in this exploratory cohort, supporting integration of biopsy tumor burden metrics into preoperative risk stratification pending external validation. Full article

Background: Prostate-specific membrane antigen (PSMA) is markedly overexpressed in prostate cancer (PCa), and there is growing evidence to support its usefulness in initial diagnostic assessments. This study compares the diagnostic performance of PSMA positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (mpMRI) in evaluating seminal vesicle invasion (SVI), extraprostatic extension (EPE), and pelvic lymph node involvement before radical prostatectomy. Methods: A retrospective, single-institution analysis was performed. From a cohort of 325 patients who underwent radical prostatectomy between June 2022 to November 2024, 85 had undergone preoperative PSMA PET/CT for intermediate- and high-risk disease at biopsy, forming our study group. Two blinded specialists, one in radiology and one in nuclear medicine, independently interpreted the scans, using histopathological results as the reference standard. The primary outcome was diagnostic accuracy for T- and N-stage classification, while the secondary outcomes included the correct identification of the index lesion and comparative performance for each modality. Results: The study cohort comprised patients with intermediate-to-high-risk prostate cancer (median age: 66 years; median PSA level: 11.6 ng/mL; median PSA density: 0.3 ng/mL/cm³). Forty-eight patients presented with an ISUP grade of 3 or higher on biopsy. PSMA PET/CT was more sensitive than MRI for detecting EPE (72.2% vs. 46.9%) and nodal metastases (91.7% vs. 8.3%). Furthermore, PSMA PET/CT demonstrated significantly higher concordance with histopathological findings in index tumor localization (76.5% vs. 67.9%, p < 0.001). An exploratory analysis revealed a potential age-dependent pattern, but this requires confirmation in larger studies. Conclusions: In this select cohort, PSMA PET/CT demonstrated greater accuracy than MRI for locoregional staging in patients with intermediate-to-high-risk prostate cancer (PCa). However, the generalizability of these findings is limited by the retrospective design and potential selection bias. These results suggest that PSMA PET/CT may have a valuable role in the initial staging workflow, but this needs to be confirmed in larger, prospective studies. An exploratory analysis suggested a potential age-dependent pattern, but this requires confirmation in larger studies. Full article

Background: This study evaluated time-dependent changes in anchor hole width (AHW) and their association with postoperative medial meniscus extrusion (MME) in patients undergoing open-wedge high tibial osteotomy (OWHTO) with medial meniscus posterior root tear (MMPRT) repair and meniscal centralization. Methods: Thirty knees treated with combined OWHTO and MMPRT repair using the centralization technique were retrospectively reviewed. MRI, CT, and second-look arthroscopy were performed preoperatively and postoperatively. AHW of the MMPRT anchor and two centralization anchors (midbody and midbody–posterior, M-anchor and MP-anchor) were measured on multiplanar reconstruction CT images at 1, 3, and 6 months, and 1 year, and their correlations with postoperative MME were analyzed. Results: AHW increased up to 3 months and gradually decreased with surrounding sclerosis by 1 year. The M-anchor showed significantly greater mediolateral (ML) expansion than the MP-anchor and demonstrated a moderate positive correlation between 1-year AHW and MME (r ≈ 0.5, p < 0.01). Second-look arthroscopy confirmed a 90% healing rate of the repaired root. Conclusions: Although OWHTO combined with MMPRT repair and centralization achieved favorable root healing, postoperative MME progression was not fully prevented. Time-dependent ML anchor hole expansion around the M-anchor may indicate persistent micromotion, elongation of the meniscotibial ligament, and degenerative stretch of the repaired meniscus following healing, suggesting that even after successful root healing, ML motion remains difficult to control, highlighting the need for biomechanically optimized fixation. Full article

Prostate cancer (PCa) is the most common malignancy in men worldwide. Multiparametric MRI (mpMRI) improves the detection of clinically significant PCa (csPCa); however, it remains limited by false-positive findings and inter-observer variability. Time-dependent diffusion (TDD) MRI provides microstructural information that may enhance csPCa characterization beyond standard mpMRI. This prospective observational diagnostic accuracy study protocol describes the evaluation of PROS-TD-AI, an in-house developed AI workflow integrating TDD-derived metrics for zone-aware csPCa risk prediction. PROS-TD-AI will be compared with PI-RADS v2.1 in routine clinical imaging using MRI-targeted prostate biopsy as the reference standard. Full article

A group of experts of different specialties involved in the care of prostate cancer (PCa) patients participated in the ENFOCA2 project, promoted by the Spanish Oncology Genitourinary Group (SOGUG), with the aim to review, discuss, and summarize current relevant aspects related to screening, diagnosis, imaging, risk-based approach, and molecular characterization of PCa. A multidisciplinary team (MDT) approach is essential to ensure that patients receive evidence-based care, promoting shared decision-making, and tailoring treatment to the patient’s unique values and preferences. Population-based screening based on risk-stratified algorithms is needed to overcome the limitations of opportunistic screening for detecting clinically significant PCa. Next-generation imaging (NGI) methods, such as prostate-specific membrane antigen (PSMA) PET/CT alone or combined with multiparametric MRI (mpMRI), have a promising role in different scenarios of the diagnostic process due to their high sensitivity. The diagnostic yield of mpMRI should be improved, especially for assessing extraprostatic extension. The use of specific molecular probes as imaging markers for MRI could improve the staging of metastatic disease. Protocols for germline testing developed by international societies, such as the European Association of Urology (EAU) and the National Comprehensive Cancer Network (NCCN), should be adapted at local levels, with BRCA1/2, ATM, PALB2, CHEK2, MLH1, MSH2, MSH6, PMS2, EPCAM, and HOXB13 as the genes to be investigated. Genomic classifier tools help identifying aggressiveness of cancers and aid in personalized treatment decision-making. Joint efforts of multidisciplinary physicians are crucial to improve health outcomes for patients with PCa across the spectrum of this disease. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

Introduction: PI-RADS 3 lesions represent a diagnostic grey zone on multiparametric MRI, with clinically significant prostate cancer (csPCa) detected in only 10–30%. Their equivocal nature leads to both unnecessary biopsies and missed cancers. Artificial intelligence (AI) has emerged as a potential tool to provide objective, reproducible risk prediction. This review summarises current evidence on AI for risk stratification in patients with indeterminate mpMRI findings, including clarification of key multicentre initiatives such as the PI-CAI (Prostate Imaging–Artificial Intelligence) study—a global benchmarking effort comparing AI systems against expert radiologists. Methods: A narrative review of PubMed and Embase (search updated to August 2025) was conducted using terms including “PI-RADS 3”, “radiomics”, “machine learning”, “deep learning”, and “artificial intelligence.” Eligible studies included those evaluating AI-based prediction of csPCa in PI-RADS 3 lesions using biopsy or long-term follow-up as reference standards. Both single-centre and multicentre studies were included, with emphasis on externally validated models. Results: Radiomics studies demonstrate that handcrafted features extracted from T2-weighted and diffusion-weighted imaging can distinguish benign tissue from csPCa, particularly in the transition zone, with area-under-the-ROC curves typically 0.75–0.82. Deep learning approaches—including convolutional neural networks and large-scale representation-learning frameworks—achieve higher performance and can reduce benign biopsy rates by 30–40%. Models that integrate imaging-based AI with clinical predictors such as PSA density further improve discrimination. The PI-CAI study, the largest international benchmark to date (>10,000 MRI exams), shows that state-of-the-art AI systems can match or exceed expert radiologists for csPCa detection across diverse scanners, centres, and populations, though prospective validation remains limited. Conclusions: AI shows strong potential to refine management of PI-RADS 3 lesions by reducing unnecessary biopsies, improving csPCa detection, and mitigating inter-reader variability. Translation into routine practice will require prospective multicentre validation, harmonised imaging protocols, and integration of AI outputs into clinical workflows with clear thresholds, decision support, and safety-net recommendations. Full article

Background and Objectives: Multiparametric MRI (mpMRI) has improved prostate cancer (PCa) detection, but the added value of cognitive fusion (CF) over systematic biopsy (SB) remains debated. This prospective study evaluated the diagnostic performance of SB, CF, and their combined use in patients with Prostate Imaging and Reporting Data System (PI-RADS) ≥3 lesions. Materials and Methods: A total of 282 patients underwent mpMRI followed by both SB and CF biopsy. Results: PCa was diagnosed in 154 patients. SB detected 112 cancers (24 ISUP 1 (International Society of Urological Pathology), 88 ISUP ≥ 2), and CF detected 135 cancers (16 ISUP 1, 119 ISUP ≥ 2), while the combined approach detected all 154 cancers (9 ISUP 1, 145 ISUP ≥ 2). CF identified 42 cancers missed by SB, whereas SB identified 19 cancers not detected by CF. CF upgraded 38 patients from low-risk to intermediate-risk (23)/high-risk (15) categories, while SB underestimated disease severity in 41 cases. No major biopsy-related complications were recorded. Conclusions: CF biopsy outperformed SB in detecting clinically significant PCa and improved risk stratification, while the combined approach provided the highest overall diagnostic performance, supporting its use in contemporary PCa assessment. Full article

Introduction. This study aims to conduct a cost-effectiveness analysis comparing two primary treatment approaches: radical prostatectomy versus radiotherapy plus androgen deprivation therapy (ADT) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions. Patients and Methods. Data were extracted from two published retrospective cohort studies. Using survival data from two retrospective studies, we reconstructed Kaplan–Meier curves, overlaid them for comparative metasurvival analysis, and developed a cost-function model to assess economic implications alongside clinical outcomes. The primary outcomes included biochemical recurrence-free survival (FFBF) at 2 and 5 years; the area under the survival curve; total cost per treatment strategy; and cost per recurrence-free patient at 5 years. Results. At 5 years, the estimated FFBF was 83% for radiotherapy vs. 28% for prostatectomy. Radiotherapy yielded an AUC of 80.7, while prostatectomy showed 41.9. Radiotherapy yielded a cost of 21,211 € per FFBF patient compared to 113,730 € for prostatectomy. Conclusion. Our study demonstrates that radiotherapy combined with ADT, when selected based on mpMRI stratification, may represent a cost-efficient alternative, pending prospective validation. To radical prostatectomy in patients with PI-RADS 5 prostate cancer, with a favourable cost–benefit profile. Full article