Emerging imaging modalities are reshaping the evaluation of metastatic castration-resistant prostate cancer, with dual-tracer and AI-powered PSMA PET/CT advancing diagnostic precision beyond traditional approaches, although these technologies may face limitations such as cost, accessibility, and increased radiation exposure.
Oncologists managing mCRPC face persistent challenges: conventional imaging often underestimates heterogeneous tumor biology, delaying adjustments to androgen receptor signaling inhibitors and other systemic therapies. The capacity to accurately assess treatment response is critical for optimizing cancer treatment outcomes and tailoring individualized care.
Recent real-world data demonstrate that combining 68Ga-RM26 with 68Ga-PSMA−617 in a single PSMA PET/CT session yields enriched diagnostic data that capture complementary aspects of tumor physiology, supporting more nuanced treatment evaluations. By combining different PSMA PET/CT radiotracers with GRPR tracers like 68Ga-RM26, clinicians can identify residual disease that may evade detection by a single-tracer protocol, thereby strengthening precision in PSMA imaging and informing subsequent therapeutic decisions.
In practical terms, dual-tracer imaging reveals lesions with variable expression profiles—some lesions demonstrate high PSMA affinity while others preferentially bind gastrin-releasing peptide receptor tracers such as 68Ga-RM26. This broader molecular portrait enables oncologists to pinpoint candidates likely to benefit from radioligand therapy or to escalate systemic regimens earlier, ultimately improving androgen receptor signaling inhibitor (ARSI) outcomes.
Parallel advances in image analysis harness artificial intelligence to automate quantitative assessments. Integrating AI algorithms into [18F]PSMA-1007 PET/CT facilitates precise, automated evaluation of split renal function, reducing interobserver variability and enhancing the safety planning of nephron-sparing interventions. A recent study on AI-based measurement techniques demonstrated superior consistency compared with manual methods, marking a shift toward more reliable functional metrics in prostate cancer PET.
Beyond renal assessment, AI-driven platforms are being trained to detect subtle patterns in tracer uptake that correlate with early therapeutic resistance. Earlier findings suggest these tools could soon support decision-making by flagging lesions at risk for progression before they are clinically apparent, streamlining follow-up protocols and optimizing resource allocation.
For oncologists, adopting these innovations means rethinking imaging protocols: scheduling dual-tracer studies for patients with equivocal single-tracer scans, and integrating AI workflows to standardize quantitative readouts. Embracing these technologies may not only enhance the accuracy of mCRPC assessment but also accelerate transitions to alternative therapies when warranted, reducing time lost to ineffective interventions.
Ongoing multicenter trials will be essential to define standardized dual-tracer protocols and to validate AI algorithms across diverse populations and imaging platforms. As evidence continues to accumulate, it will be crucial to update practice guidelines and to train multidisciplinary teams in these advanced imaging strategies.
Key takeaways:
- Enhanced Diagnostic Precision: Enriches evaluation by capturing complementary tracer uptake patterns in mCRPC.
- Advanced AI Integration: Automates split renal function assessment and identifies early signs of therapeutic resistance.
- Clinical Impact: Informs timely adjustments to systemic therapies and radioligand candidacy.
- Future Directions: Requires protocol standardization and broader validation to integrate into routine workflows.