University of Lynchburg DMSc Doctoral Project Assignment Repository
Advisor
Dr. Lawrence Herman, DMSc, PA-C, DFAAPA
Abstract
Risk stratification is central to the management of localized prostate cancer; however, traditional clinical and pathologic markers incompletely capture tumor biology within established risk categories. This limitation is most pronounced in intermediate-risk disease, where treatment decisions are complex and frequently influenced by patient preferences. As therapeutic options range from active surveillance to multimodality treatment, more precise tools are needed to better characterize tumor behavior and guide individualized decision-making.
A focused literature review was conducted using PubMed and Elicit to identify peer-reviewed studies evaluating genomic classifiers and artificial intelligence (AI)–based predictive tools in patients with intact localized prostate cancer. Studies involving post-radical prostatectomy populations were excluded to maintain relevance to initial treatment planning.
Genomic classifiers such as Decipher (Veracyte, Inc., South San Francisco, CA) provide prognostic insight into tumor biology by assessing molecular features associated with disease progression and estimating the risk of biochemical recurrence, distant metastasis, and prostate cancer–specific mortality. In contrast, AI-based tools such as ArteraAI (Artera, Inc., Los Altos, CA) offer predictive guidance regarding treatment response, particularly the benefit of adding short-term androgen deprivation therapy to radiation. Together, these technologies represent a shift from assessing what a tumor looks like to understanding how it behaves and how it may respond to therapy.
When applied within National Comprehensive Cancer Network (NCCN)–defined risk categories, genomic and AI-based tools refine rather than replace traditional risk assessment. Their integration is particularly impactful in intermediate-risk disease, where clinical uncertainty is common, and the consequences of over- and undertreatment are substantial.
Incorporating these tools into guideline-concordant care supports more precise, patient-centered, risk-adapted treatment decisions, enhances clinician confidence, and supports a more balanced and individualized approach to therapeutic intensity in localized prostate cancer.
Recommended Citation
Johnson H. Genomic and AI-based Decision-Support Tools in Localized Prostate Cancer: Clinical Interpretation of Decipher and ArteraAI. University of Lynchburg DMSc Doctoral Project Assignment Repository. 2026; 8(1).
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