Digital twin technologies in prostate cancer as a frontier for precision medicine

Clinical Snapshot

Key Findings

• P Value: Not applicable — no statistical analyses performed

• Effect Size: Not applicable — no empirical effect sizes reported

• Primary Outcome: No primary quantitative outcome reported. The paper conceptually proposes that digital twin technologies could enable personalised, predictive prostate cancer management by integrating multimodal patient data.

• Nnt Or Sensitivity: Not applicable — no NNT, sensitivity, specificity, or hazard ratios reported. All potential benefits are conceptual and unvalidated in clinical trials.

• Confidence Interval: Not applicable — no confidence intervals reported

Clinical Application

Currently low in routine clinical practice. Digital twin implementation requires substantial investment in interoperable electronic health record systems, high-resolution multiparametric MRI and molecular imaging pipelines, genomic and liquid biopsy data integration, real-time computational infrastructure, and regulatory-grade validation frameworks. These prerequisites are not yet met in most clinical environments globally. In the Australian context, prostate cancer is the most commonly diagnosed cancer in men, with approximately 24,000 new cases annually (Cancer Australia data). The RACGP and Cancer Council Australia guidelines emphasise shared decision-making around PSA screening and active surveillance, areas where DT-assisted personalisation could theoretically add value. However, no DT-based tools are currently TGA-approved or PBS-listed for prostate cancer management. The Australian Digital Health Agency's national digital health strategy and My Health Record infrastructure provide a foundational data layer, but interoperability gaps remain significant. Implementation would require NHMRC-funded validation trials and TGA Software as a Medical Device (SaMD) regulatory pathways before clinical adoption. Health equity considerations are particularly salient in rural and remote Australia, where digital health infrastructure disparities could exacerbate existing access inequalities. Theoretically applicable to all men with prostate cancer across the disease spectrum — from active surveillance candidates to those with metastatic castration-resistant disease. Most immediately relevant to academic and tertiary centres with advanced imaging, genomic, and data infrastructure capabilities.

Abstract

Prostate cancer (PCa) is the most frequently diagnosed malignancy among men and presents major clinical and socioeconomic challenges worldwide. Despite advances in early detection, imaging, and therapy, managing PCa remains complex due to disease heterogeneity, risks of overdiagnosis, and overtreatment. Digital twin (DT) technologies might represent an emerging conceptual framework aimed at supporting dynamic, patient-specific virtual modeling for personalized clinical decision-making. By integrating multimodal clinical, imaging, molecular, and physiological data, DTs can simulate disease progression, predict treatment responses, and support proactive, adaptive care. This perspective explores the conceptual framework for DT ecosystems in PCa, highlighting potential clinical impacts, infrastructural requirements, and barriers to implementation. Harnessing DTs could impact PCa management into a truly predictive, personalized, and participatory approach, improving outcomes and optimizing healthcare resource utilization globally. RELEVANCE STATEMENT: DT technologies may enable personalized, predictive PCa management by integrating multimodal patient data to guide diagnosis, treatment selection, and monitoring, with the potential to improve outcomes, reduce overtreatment, and optimize healthcare resource utilization KEY POINTS: DTs create virtual patient models to personalize PCa care. They integrate imaging, clinical, and molecular data into one system. This approach may reduce overdiagnosis and unnecessary treatments.

References

1. 1.Pecoraro, M., Messina, E., Novelli, S., Laschena, L., Blasilli, G., Tronci, E., & Panebianco, V. (2026). Digital twin technologies in prostate cancer as a frontier for precision medicine. European Radiology Experimental. https://doi.org/10.3390/app12168156