Beyond Biopsy: Enhancing Prostate Cancer Risk Assessment

Historically, risk assessment centered on biopsy pathology; today, contemporary pathways integrate PSA, DRE, validated risk calculators, and pre-biopsy mpMRI alongside biopsy findings to guide staging and management.

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a promising tool; in practice, mpMRI findings such as PI-RADS scores and radiomic features are combined with clinical variables by clinicians or validated models to enhance risk assessment and detection of clinically significant prostate cancer. These combined inputs improve well-established endpoints such as detection of clinically significant disease, local staging, and estimation of extracapsular extension risk, thereby informing individualized treatment planning.

The shift towards genomic testing is evident as it refines risk stratification, informing decisions on active surveillance for prostate cancer. In current guidelines, select tissue-based genomic classifiers are used as adjuncts to, not replacements for, clinicopathologic risk groups when considering active surveillance. Genomic markers offer prognostic information that complements traditional methods, helping to identify candidates for less aggressive treatments; peer-reviewed validations and guideline statements underpin this adjunctive use.

Liquid biopsies represent an emerging approach for serial, minimally invasive monitoring—using, for example, circulating tumor cells (CTCs) or circulating tumor DNA (ctDNA)—with the strongest evidence in advanced disease and primarily investigational use in localized prostate cancer. In advanced and metastatic settings, this approach can inform treatment selection and monitoring in select contexts, whereas in localized disease it remains largely investigational.

PSMA PET imaging stands out for its enhanced sensitivity and specificity in appropriate settings—particularly biochemical recurrence and initial staging of unfavorable intermediate- and high-risk disease—offering critical information for precise treatment planning. In practice, PSMA PET improves detection of nodal and distant metastases compared with CT and bone scan, while mpMRI remains the primary modality for mapping intraprostatic tumor extent.

Despite these advances, barriers such as accessibility and cost impede widespread adoption, particularly in rural areas. Examples include evolving reimbursement for PSMA PET, variable payer coverage for genomic classifiers, the need for high-quality scanners and experienced readers for mpMRI, and data/IT infrastructure requirements for machine learning tools.

Looking forward, multi-modal diagnostics can meaningfully improve prostate cancer care if paired with prospective evidence generation, guideline integration, cost-effectiveness evaluations, equitable reimbursement and access, and workforce training to ensure real-world implementation—addressing the barriers noted above.

Key Takeaways:

• Pre-biopsy evaluation and staging have become multimodal—combining PSA, DRE, validated risk calculators, mpMRI, and, in appropriate risk groups, PSMA PET.
• Validated genomic classifiers can complement clinicopathologic risk groups to inform active surveillance and related decisions as adjuncts, not replacements.
• Machine learning approaches are promising for tasks like ECE risk estimation but remain investigational and dependent on external validation and data infrastructure.
• Impact will hinge on access, reimbursement, workforce expertise, and implementation—areas that require coordinated investment to translate advances into equitable care.