Linking Artificial Intelligence (AI)–derived risk phenotypes to prostate cancer care pathways: Implications for treatment intensity and healthcare operations

Efficient prostate cancer management relies on risk stratification to inform clinical decision-making, care planning, and resource allocation. However, existing approaches often fail to incorporate treatment intensity, care pathways, and downstream resource utilization needed to support healthcare systems. Using data from 4,579 men enrolled in a randomized controlled trial, this study applies STRATA-PC, a treatment-aware, survival-informed phenotyping framework that leverages longitudinal prostate-specific antigen (PSA) and digital rectal examination (DRE) trajectories along with baseline clinical characteristics to derive clinically interpretable risk phenotypes and examine how data-driven risk groups relate to real-world prostate cancer care pathways and resource utilization. STRATA-PC demonstrates strong discrimination for overall survival and prostate cancer–specific mortality and identifies three well-separated phenotypes. The low-risk phenotype (C0) is characterized by surveillance-dominated care pathways with delayed or no curative treatment, reflecting low immediate clinical urgency and sustained outpatient monitoring. The high-risk phenotype (C1) exhibits early initiation of definitive treatment, greater use of multimodal therapy, and a higher likelihood of post-treatment escalation, indicating concentrated near-term demand and increased coordination complexity. The intermediate-risk phenotype (C2) demonstrates delayed treatment initiation with a higher propensity for late multimodal escalation following extended monitoring, representing a transitional care pathway with deferred but more complex downstream resource utilization. This study examines STRATA-PC-derived phenotypes from a healthcare operations perspective by comparing survival risk, symptom burden, treatment modality selection, escalation behavior, and timing of care initiation. These findings show how patient-level risk heterogeneity translates into structured variation in care pathways and healthcare demand, providing actionable insight for resource planning.