PSA circulates in the bloodstream in multiple molecular forms, and assays that quantify specific isoforms have consistently demonstrated superior cancer specificity when compared with the measurement of tPSA alone. Among these, the PHI and the 4Kscore test have emerged as the most extensively validated and clinically established tools. Both integrate traditional clinical parameters with novel serum measurements to generate a personalized, quantitative risk assessment.

The PHI is a sophisticated mathematical algorithm that combines the serum levels of tPSA, free PSA (fPSA) and the proenzyme precursor isoform [-2] proPSA, the latter being more notably associated with malignant prostatic tissue. Numerous studies have validated its superior diagnostic accuracy over tPSA and %fPSA alone, particularly in the diagnostic ‘gray zone’ (PSA 2–10 ng/ml). Meta-analyses and large prospective cohort studies consistently report that PHI notably improves specificity for csPCa (Gleason Grade Group ≥2, intermediate- or high-risk disease) while maintaining high sensitivity, thereby potentially avoiding 20–30% of unnecessary biopsies (37–39). For instance, a multicenter European and Asian study highlighted the need for population-specific PHI reference ranges but confirmed its robust diagnostic performance across diverse settings (40). Furthermore, PHI demonstrates prognostic value, predicting pathological outcomes at radical prostatectomy and grade reclassification during active surveillance (41–44). Cost-effectiveness analyses suggest that PHI-guided biopsy strategies can be economically favorable by reducing low-yield procedures (45,46). Notably, PHI has been evaluated as an effective ‘gatekeeper’ to mpMRI; for example, a PHI threshold can stratify men to undergo MRI or proceed directly to biomarker-guided decision-making, optimizing resource utilization (47–49).

The 4Kscore test measures serum levels of four kallikreins (tPSA, fPSA, intact PSA and human kallikrein 2) and combines them with clinical variables (age, digital rectal exam findings, prior biopsy status) to calculate the percentage risk of detecting csPCa on biopsy. Its development and validation, including data from the ERSPC and the STHLM3 study, have shown it to be a powerful tool for pre-biopsy risk stratification (50,51). The primary utility of the test lies in its ability to identify men with a low risk (<7.5%) of csPCa, for whom biopsy can be safely deferred (52,53). Prospective trials, such as the PROBASE screening study, have incorporated the 4Kscore to guide biopsy decisions in men with elevated PSA, demonstrating its practical application in reducing overdiagnosis (50). Recent data from the GÖTEBORG-2 screening trial reaffirmed its performance as a reflex test, notably outperforming tPSA in csPCa detection (24). However, discussions continue regarding the optimal risk threshold for clinical action, and the development of ‘4Kscore density’ (adjusting for prostate volume) is being explored to further refine its predictive power (54,55).

Beyond PSA derivatives, the analysis of tumor-derived material in blood, known as liquid biopsy, represents a frontier in oncology. While its most established role is in advanced, metastatic disease, its potential for early detection and screening is under intense investigation.

In metastatic castration-resistant PCa, the enumeration of CTCs and analysis of cfDNA are validated prognostic tools and can guide therapy by identifying actionable genomic alterations (for example in BRCA1/2 and androgen receptor) (56–59). The clinical utility in this setting is well-documented for monitoring treatment response and understanding resistance mechanisms (60,61). However, their application in early-stage, localized disease is challenged by the low abundance of tumor-derived material in the bloodstream. Studies have confirmed that levels of cfDNA and CTCs are markedly lower in patients with localized PCa compared with those with metastatic disease (62,63). Despite this limitation, highly sensitive detection techniques are beginning to exhibit clinical potential in localized disease. For example, specific fragmentation patterns of cfDNA (fragmentomics) or the presence of tumor-informed mutations can detect localized cancers with promising sensitivity (64–66). Research indicates that the detection of circulating tumor DNA (ctDNA) in patients with clinically localized disease is associated with a higher risk of rapid biochemical recurrence, suggesting its prognostic, if not yet diagnostic, potential in early stages (46,63).

DNA methylation is a common and early event in prostate carcinogenesis. Hypermethylation of gene promoters, such as glutathione S-transferase Pi 1 (GSTP1), is notably specific for PCa tissue. Detecting these methylated DNA sequences in plasma or serum cfDNA is therefore a promising diagnostic strategy. Technical feasibility has been demonstrated in multiple studies, showing that methylated GSTP1 can be detected in the blood of patients with PCa with high specificity, albeit with variable sensitivity that is often associated with tumor stage and volume (67–69). The challenge for screening applications is achieving sufficient sensitivity for low-volume, organ-confined disease. To address this, efforts have been focused on generating multi-gene methylation panels. Assays analyzing a combination of methylation markers (for example GSTP1, Ras association domain-containing protein 1 and APC) have shown improved sensitivity for detecting localized PCa compared with single markers (70,71). More advanced approaches use tissue-informed, genome-wide methylation profiling of cfDNA to identify cancer-specific signatures, even distinguishing aggressive histologic subtypes such as neuroendocrine differentiation (72,73). While these epigenetic blood tests require rigorous validation in screening populations, they represent a compelling direction for developing highly specific, non-invasive screening tools.

PCA3 is a long non-coding RNA that is markedly upregulated in PCa tissue compared with benign prostate cells, offering inherent cancer specificity (78). Its measurement in post-digital rectal exam (post-DRE) urine captures RNA released from prostate cells, providing a quantifiable PCA3 score.

The primary clinical utility of PCA3 has been established in the repeat biopsy setting for men with a prior negative biopsy. Meta-analyses confirm that a PCA3 score can help discriminate between patients who may benefit from a repeat biopsy and those who can be safely monitored (76). A landmark study by Newcomb et al (79) within the Canary Prostate Active Surveillance Study prospectively evaluated PCA3 in an active surveillance cohort. This study provided notable evidence that PCA3 scores were associated with the risk of pathological grade reclassification. By demonstrating its potential to monitor disease progression in men opting for surveillance, this research expanded the role of PCA3 beyond diagnostic triage to encompass risk stratification within management pathways. However, its performance for primary screening or initial biopsy decision-making is more limited. While PCA3 demonstrates good specificity, its sensitivity for detecting csPCa, particularly in biopsy-naïve men, is often considered insufficient as a standalone test. This limitation is attributed to variable RNA stability and the moderate association of the test with tumor aggressiveness. Consequently, PCA3 is frequently integrated into multivariable risk models or combined with other markers to enhance its predictive accuracy.

The TMPRSS2: ERG gene fusion is a seminal genetic alteration present in ~50% PCas, offering high specificity for malignant prostatic epithelium (80). Its detection in urine provides a direct tumor-derived signal. Beyond mere detection, the presence and quantitative level of TMPRSS2: ERG mRNA in urine have been associated with indicators of tumor aggressiveness, such as higher Gleason grade (81).

The clinical strength of TMPRSS2: ERG lies not in its standalone use but in its combination with other biomarkers. This is exemplified by the commercially available Mi-Prostate Score (MiPS) test, which integrates urinary PCA3, TMPRSS2: ERG and serum PSA into a single risk algorithm. A pivotal validation study by Tosoian et al (82) demonstrated the robust clinical utility of the MiPS test. In a multicenter cohort of men referred for biopsy, the study showed that MiPS notably improved the prediction of csPCa (Gleason ≥7) compared with standard clinical variables alone (83). Notably, it exhibited a high negative predictive value, effectively ruling out high-grade disease and potentially reducing unnecessary biopsies by 30–40% while maintaining high sensitivity for aggressive cancers. This study underscored the principle that combining markers with complementary characteristics, the sensitivity of PCA3 and the specificity of TMPRSS2: ERG, within a mathematical model yields superior diagnostic accuracy and clearer clinical action thresholds than any single marker.

To address the complexity and heterogeneity of PCa, advancements have focused on multiplexed urinary panels that analyze multiple RNA biomarkers simultaneously, aiming for higher diagnostic precision.

SelectMDx is a post-DRE urine test that measures the mRNA expression of homeobox C6 and distal-Less homeobox 1, two homeobox genes involved in oncogenesis, combined with clinical risk factors (84,85). Key validation studies in European biopsy-naïve cohorts have consistently shown that SelectMDx possesses a high negative predictive value (NPV), often >90% for high-grade PCa (84,85). The prospective multicenter study by Hendriks et al (85) was instrumental in defining its clinical role (85). This study evaluated SelectMDx with or without mpMRI in >500 biopsy-naïve men and confirmed its high NPV. It demonstrated that SelectMDx could effectively identify men at markedly low risk of csPCa, potentially allowing 30–40% of patients to avoid immediate biopsy. This solidified its position primarily as a robust ‘rule-out’ test to reduce unnecessary procedures.

ExoDx Prostate (IntelliScore) (EPI) analyzes exosome-derived RNA from a first-catch, non-DRE urine sample, evaluating an expression signature to provide an EPI. The prospective validation study by Tutrone et al (86) specifically focused on men in the diagnostic gray zone (PSA 2–10 ng/ml), a group where clinical decision-making is most challenging. The study demonstrated that the EPI score independently predicted the likelihood of csPCa and high-grade disease. Notably, it showed that physician biopsy recommendations changed in ~30% of cases based on the EPI score, predominantly leading to a reduction in biopsies for low-risk scores. This study highlighted the practical impact of the test on clinical decision-making in a targeted population.

The evolution of the MyProstateScore (MPS) test exemplifies progress in panel development; initial versions combined PCA3 and TMPRSS2: ERG (82). The comprehensive development and validation study by Tosoian et al (87) for MPS 2.0 marked a notable advance (87). MPS 2.0 utilizes an expanded 18-gene panel and was validated across multiple cohorts. A key finding was its excellent performance in men with equivocal MRI findings, a common clinical dilemma. The test accurately stratified risk within this group, helping to resolve uncertainty and guide biopsy decisions. This study underscores the trend towards more complex gene signatures to improve diagnostic accuracy, particularly in challenging clinical scenarios. Other research explores panels of long non-coding RNAs (88,89), microRNAs from urine extracellular vesicle (EV) (90,91) and methylation markers (92), showing promising diagnostic and prognostic potential.

Beyond analytical performance, real-world implementation of urinary biomarkers is markedly influenced by pre-analytical variables. The diagnostic accuracy of PCA3 and TMPRSS2: ERG, for example, is substantially enhanced by a vigorous DRE before urine collection, which exfoliates prostatic cells (78,79). However, the lack of standardization in DRE technique (pressure, duration, number of strokes) introduces notable variability across clinics and studies, potentially affecting test reproducibility and clinical decision thresholds. This variability has been highlighted as a barrier to widespread adoption (77). Sample stability represents another practical hurdle as urinary RNAs degrade over time, and variations in storage temperature, time to processing and the use of preservative buffers can alter measured biomarker levels (86). The ExoDx Prostate test, which uses a first-catch, non-DRE urine sample, was specifically developed to circumvent DRE-related variability (86). Furthermore, differences in urine collection protocols (first-catch vs. post-DRE vs. extended stream) and centrifugation steps affect cellular content and RNA yield. These pre-analytical factors, if not harmonized, limit cross-study comparability and hinder the widespread adoption of urinary biomarkers in routine practice. Ongoing efforts to develop standardized operating procedures and commercial kits with built-in stabilization are essential to translate these promising markers into reliable clinical tools (36,77).

A total of 10–15% of PCas exhibit a marked hereditary component, often driven by pathogenic variants in high-penetrance genes involved in DNA damage repair (DDR) and other notable pathways (93,94). The most clinically established among these are mutations in BRCA1 and BRCA2, which are notably associated not only with breast and ovarian cancers but also with an increased risk of aggressive PCa. Carriers of BRCA2 mutations face a markedly elevated lifetime risk of PCa (estimated 2- to 8-fold higher than non-carriers), earlier age of onset and a greater likelihood of developing high-grade, metastatic disease (93). Similarly, mutations in homeobox B13, particularly the G84E variant, are linked to hereditary PCa, especially in families of European descent (95). Other relevant genes include those involved in mismatch repair [MutL homolog 1, MutS homolog (MSH)2, MSH6 and post meiotic segregation increased 2] associated with Lynch syndrome, a hereditary cancer predisposition syndrome that increases the risk of several malignancies, including PCa. Men with Lynch syndrome, particularly carriers of MSH2 mutations, have a notably elevated risk of developing PCa, often with earlier onset and more aggressive features, which underscores the importance of tailored screening in this population (93,96). Other relevant genes include ataxia-telangiectasia mutated, checkpoint kinase 2 and partner and localizer of BRCA2 (97–99). The evidence for these genes derives primarily from large case-control studies and meta-analyses, with prospective validation for mismatch repair genes from the IMPACT screening study. These genetic markers are already integrated into clinical practice through guideline-recommended germline testing panels (for example NCCN guidelines) and are being translated into targeted screening protocols for high-risk populations (100).

The clinical imperative lies in identifying these mutation carriers to implement targeted screening and management strategies. Evidence from prospective studies supports this approach. For example, the international IMPACT study prospectively evaluated PSA screening in men with known pathogenic mutations in mismatch repair genes (such as MSH2, MSH6) (96). Interim results demonstrated a higher incidence of PCa in carriers compared with non-carrier controls, with a notable proportion being intermediate- or high-risk disease. This study validates the rationale for tailored screening in this genetically defined high-risk cohort and highlights the importance of identifying Lynch syndrome families for proactive management (96).

Furthermore, the presence of germline DDR alterations, particularly in BRCA2, has marked therapeutic implications in advanced disease, predicting sensitivity to poly (ADP-ribose) polymerase inhibitors such as olaparib and rucaparib (101–103). This dual utility, informing both early detection and treatment selection, underscores the importance of germline genetic testing, especially in men with a strong family history, early-onset disease, or advanced/metastatic PCa (98,100,104). However, challenges persist, including equitable access to genetic counseling and testing, variability in mutation prevalence across ancestries and the need for clear clinical guidelines on who to test and how to manage identified carriers.

In contrast to rare, high-impact mutations, the majority of inherited PCa risk is attributed to the combined effect of hundreds to thousands of common single-nucleotide polymorphisms (SNPs), each conferring a markedly small individual risk. A PRS aggregates these effects into a single quantitative measure of genetic predisposition. PRS are developed through genome-wide association studies that identify risk-associated SNPs and their effect sizes, typically derived from large, predominantly European ancestry biobanks (105,106). The predictive power of a PRS is directly related to the number of included SNPs and the ancestral match between the derivation and target populations.

A key application of PRS is to augment traditional risk factors, particularly PSA levels, to improve the prediction of csPCa. Evidence from large cohort studies demonstrates that PRS can independently predict PCa risk and, when combined with PSA, notably improve risk stratification compared with PSA alone. For instance, a study by McHugh et al (30) assessed a PRS in a screening context, finding that it could identify men with a risk profile justifying earlier or more intensive screening, while others might defer screening. This prospective assessment highlights the potential of PRS to personalize screening initiation and intensity in a population-based setting. Similarly, data from the STHLM3 trial showed that a risk model incorporating a genetic score (alongside clinical variables and plasma protein markers) could reduce unnecessary biopsies while maintaining sensitivity for high-grade disease (107). The integration aims to create a ‘genetically adjusted’ risk threshold. For example, a man with a high PRS might warrant biopsy at a lower PSA level, whereas a man with a markedly low PRS could be monitored even with a borderline-elevated PSA.

Trans-ancestry meta-analyses have been crucial in developing PRS that perform better across diverse populations, although performance disparities between ancestral groups remain a concern (105,106,108). Recent advancements also explore the use of PRS to predict disease aggressiveness and outcomes. For example, Goss et al (109) demonstrated that a higher PRS was associated with an increased risk of disease upgrading (grade group reclassification) during active surveillance, suggesting its potential role in monitoring strategies. While promising, the widespread clinical implementation of PRS faces hurdles, including standardization of score calculation, integration into electronic health records and clinical decision support tools and ensuring equitable application across diverse patient populations to avoid exacerbating health disparities.

The underrepresentation of non-European ancestry groups in PRS derivation studies remains a notable barrier to equitable PCa screening (105,106,108). Without dedicated efforts to include diverse populations in biomarker discovery and validation, there is a risk that PRS and other genetic tools may inadvertently widen existing health disparities by providing less accurate risk estimates for minority groups (110). Therefore, future research should prioritize trans-ancestry genome-wide association studies, develop ancestry-calibrated risk scores and ensure that clinical validation cohorts reflect the full spectrum of population diversity. Such inclusive approaches are essential to translate the promise of personalized screening into benefit for all men, regardless of genetic ancestry.

The incorporation of mpMRI into the diagnostic pathway has markedly improved the detection of csPCa while reducing the diagnosis of indolent disease. However, mpMRI is resource-intensive and not universally accessible. Consequently, biomarkers are increasingly evaluated as tools to triage patients for MRI or to complement imaging findings, thereby creating a more efficient, sequential diagnostic workflow.

Using biomarkers to select men most likely to benefit from MRI can reduce unnecessary scans and lower costs. The PHI has been studied extensively in this role. For instance, studies have shown that applying a PHI threshold can effectively stratify men with elevated PSA, where those with low PHI scores may avoid immediate MRI, thereby optimizing healthcare resource utilization (111,112). Similarly, the 4Kscore has demonstrated utility in identifying men at low risk of csPCa, for whom MRI might be deferred (113,114). Evaluations, including cost-effectiveness analyses, support the economic viability of such biomarker-first approaches, particularly in screening populations with a low prevalence of csPCa (23,115). Nonetheless, the optimal threshold for these biomarkers remains a subject of ongoing research, as variations in study populations and assay methodologies can influence performance.

A sequential strategy employs biomarkers for initial risk stratification, followed by mpMRI and targeted biopsy for those at intermediate or high risk. This approach was exemplified in the STHLM3-MRI trial, where a blood-based risk model (incorporating protein biomarkers, genetic data and clinical variables) was used to select men for MRI. This strategy notably reduced the number of MRI scans and biopsies while maintaining high sensitivity for csPCa compared with a standard PSA-based pathway (116,117). Similarly, urinary biomarkers such as SelectMDx and ExoDx Prostate have been evaluated in combination with mpMRI. Studies indicate that these biomarkers can effectively identify men with a low likelihood of csPCa, who may safely avoid immediate MRI or biopsy (118–120). The integration of biomarkers with mpMRI thus allows for a more tailored diagnostic process, potentially enhancing both clinical outcomes and cost-efficiency.

Risk-calculator models integrate multiple variables, including clinical parameters, imaging findings and biomarker results, to generate personalized risk estimates for csPCa. These models aim to move beyond binary test results and provide a more nuanced assessment to guide biopsy decisions.

Several validated risk calculators now incorporate novel biomarkers. The rotterdam PCa risk calculator, for example, has been updated to include PHI and the 4Kscore, improving its predictive accuracy for csPCa (121,122). Similarly, the ERSPC risk calculator has been enhanced with kallikrein markers, demonstrating superior performance over PSA alone in predicting biopsy outcomes (122,123). In addition, models incorporating urinary biomarkers such as PCA3 and TMPRSS2: ERG have been developed and validated, showing improved discrimination for high-grade disease (124,125).

The strength of these integrated models lies in their ability to synthesize diverse data sources. For instance, a nomogram combining PHI with mpMRI findings, prostate imaging-reporting and data system score, has been developed and validated in biopsy-naïve men, demonstrating higher diagnostic accuracy than either modality alone (49,126). Similarly, machine learning approaches are being explored to create more sophisticated predictive models that incorporate genomic, proteomic and imaging data (127,128). However, the generalizability of these models can be limited by population-specific factors, such as differences in PSA distribution, genetic ancestry and MRI interpretation standards. Ongoing validation in diverse, prospective cohorts is essential to ensure their widespread applicability.

The adoption of novel biomarkers and advanced imaging in PCa screening must be justified not only by clinical effectiveness but also by economic viability. Health economic evaluations are crucial for informing policy decisions and clinical guidelines, particularly in resource-constrained healthcare systems.

Several cost-effectiveness analyses have evaluated biomarker-guided pathways. Studies comparing PHI- or 4Kscore-based strategies with standard PSA testing consistently demonstrate that biomarker triage can reduce the number of biopsies and MRI scans, leading to long-term cost savings (14,129). For example, a microsimulation study based on the STHLM3-MRI trial found that a biomarker-based strategy (using the Stockholm3 test) was cost-effective compared with both PSA screening alone and an MRI-based pathway (23). Similarly, economic evaluations of urinary biomarkers such as SelectMDx and ExoDx Prostate suggest that these tests can be cost-effective by avoiding unnecessary biopsies and reducing overtreatment (14,130).

The cost-effectiveness of mpMRI is notably dependent on pre-test probability. When used as a first-line test in all men with elevated PSA, mpMRI may not be cost-effective due to high upfront costs. However, when preceded by biomarker-based risk stratification (as a second-line test for higher-risk individuals), the combined pathway often proves economically favorable (115,131). Notably, these analyses also highlight the importance of context-specific factors, such as healthcare costs, biopsy complication rates and patient preferences, which can vary markedly across regions and healthcare systems. Key incremental cost-effectiveness ratio (ICER) findings from the cited literature show that PHI-guided biopsy strategies reduce unnecessary biopsies and are generally cost-saving or have ICERs <€50,000 per quality-adjusted life year (QALY) compared with standard PSA testing (45,128). Similarly, the 4Kscore-based approach yields ICERs ranging from cost-saving to ~€35,000 per QALY, depending on the risk threshold applied (13,128). The Stockholm3 test followed by MRI (STHLM3-MRI strategy) was associated with an ICER of €13,600 per QALY vs. PSA alone and was dominant (cost-saving with improved outcomes) compared with an MRI-only pathway (23).

The future of PCa screening lies in the intelligent integration of clinical, molecular and imaging data within a personalized framework. This multimodal approach acknowledges the biological heterogeneity of PCa and the diverse risk profiles of individuals. Promising directions include the development of composite scores that combine serum, urine and genetic markers (for example polygenic risk scores) with mpMRI findings to provide a comprehensive risk assessment (117,132,133). Artificial intelligence and machine learning platforms are increasingly being employed to analyze these complex datasets, potentially uncovering novel diagnostic signatures and improving predictive accuracy (128,134).

However, the implementation of such integrated pathways faces several challenges. These include the need for standardized assay protocols, equitable access to advanced diagnostics across diverse populations and the seamless integration of multimodal data into clinical workflows and electronic health records. Furthermore, robust prospective validation in population-level screening cohorts is essential to confirm the generalizability and real-world effectiveness of these strategies (35,135). Despite these hurdles, the continued evolution of integrative approaches holds great promise for refining PCa screening, ultimately aiming to maximize the detection of aggressive cancers while minimizing patient harms and healthcare costs.