Prostate-specific membrane antigen positron emission tomography/computed tomography imaging as a precision diagnostic at prostate cancer recurrence after radical prostatectomy: Modeling long-term survival.

BACKGROUND

Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT) is affecting the management of patients with prostate cancer with biochemical recurrence after radical prostatectomy. The long-term outcomes of tailoring salvage treatment on the basis of PSMA-PET/CT status remain to be determined.

METHODS

A decision-analytic model was developed to project incremental life-years of strategies that allocate treatments at biochemical recurrence after radical prostatectomy on the basis of PSMA-PET/CT status (PSMA-metastatic vs. PSMA-nonmetastatic). Modeled treatments are local/regional (radiation) or systemic (hormone therapy and doublet therapy), administered immediately or delayed. PSMA-metastatic status was assumed to lead to treatment intensification, whereas PSMA-nonmetastatic status would lead to deintensification. To project survival, data on progression to metastasis from a clinical cohort were combined with registry data on postmetastasis survival. Because of the lack of data on long-term treatment benefits by PSMA status, survival was projected by varying the hazard ratio (HR) for disease-specific death among PSMA-metastatic versus PSMA-nonmetastatic patients under delayed or local/regional regimens (HR1) and under immediate systemic regimens (HR2).

RESULTS

Mean life-years are projected to be 15.5 under the non-PSMA-tailored strategy, and mean incremental life-years range from 0.38 to 0.81 depending on HR1 and HR2. A greater benefit is projected when PSMA-metastatic status is more adverse under salvage regimens that do not include systemic agents.

CONCLUSIONS

This decision-analytic modeling study projects that PSMA-PET/CT-guided management at biochemical recurrence after radical prostatectomy yields a modest survival benefit under the specified model inputs and assumptions regarding treatment distributions. These findings may complement emerging data on the corresponding economic costs and health-related quality of life.