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.

Genomic score testing is increasingly being integrated into the management of prostate cancer (PCa) to overcome the limitations of traditional clinical and pathological parameters. Genomic tools will represent essential components of precision medicine, supporting risk stratification, therapeutic decision-making, and personalized screening strategies. Genomic score tests can be broadly classified into two main categories: polygenic risk scores (PRSs) and tumor-derived genomic classifiers (GCs). While not yet standard in routine practice, several international guidelines recommend their selective use when results are likely to impact clinical management. PRSs estimate an individual’s susceptibility to PCa based on the cumulative effect of multiple low-penetrance germline genetic variants. These scores show promise in enhancing early detection strategies and identifying men at higher genetic risk who may benefit from tailored screening protocols. Tumor-based GCs assays provide prognostic information that complements conventional clinical and pathological parameters, and are used to guide treatment decisions, including eligibility for active surveillance (AS) or adjuvant therapy after treatment of the primary tumor. This review summarizes and analyzes the current evidence on genomic testing in PCa, with a focus on the available assays, their clinical applications, and their predictive and prognostic value across the disease spectrum. When integrated with clinical and pathological parameters, these tools have the potential to significantly enhance personalized care and should be increasingly considered in routine clinical practice.

Introduction: It is unknown whether marital status affects years of life lost (YLL) in metastatic prostate cancer (mPCa) according to race/ethnicity. Methods: Within the SEER database (2004–2021), unmarried and married mPCa patients aged 40–80 years were identified. Age‐ and sex‐matched controls were generated (Social Security Administration life tables and Monte Carlo simulation). YLL were quantified for unmarried and married mPCa patients and controls according to race/ethnicity. Subsequently, multivariable competing risks regression (CRR) models were fitted to assess cancer‐specific mortality (CSM) and other‐cause mortality (OCM). Results: Among 34,202 mPCa patients, the distribution of unmarried patients according to race/ethnicity was as follows: 7267 (34.0%) in Caucasians; 3680 (57.0%) in African Americans; 1659 (37.0%) in Hispanics; and 478 (24.0%) in Asians/Pacific Islanders. YLL values in unmarried vs. married patients relative to age‐ and sex‐matched population simulated controls, were as follows: 7.7 vs. 5.8 in Caucasians (Δ: 1.9), 9.6 vs. 7.9 in African Americans (Δ: 1.7), 7.9 vs. 6.7 in Hispanics (Δ: 1.2), and 6.3 vs. 4.7 in Asians/Pacific Islanders (Δ: 1.6). In multivariable CRR models, unmarried status independently predicted higher CSM (1.2‐fold, p < 0.001) and OCM (1.2‐fold, p < 0.001) in Caucasians, only higher CSM in African Americans (1.1‐fold, p = 0.008) and in Asians/Pacific Islanders (1.2‐fold, p = 0.02), but only higher OCM in Hispanics (1.5‐fold, p < 0.001). Conclusion: Unmarried mPCa patients exhibited higher YLL values than their married counterparts, relative to age‐ and sex‐matched population simulated controls, across all races/ethnicities. Interestingly, the YLL detriments originated from both CSM and OCM in Caucasians, only CSM in African Americans and Asians/Pacific Islanders, and only OCM in Hispanics.