PSA dynamics: Enhancing predictive models in prostate cancer follow-up.

Abstract

424 Background: Advances in prostate cancer (PC) care have led to a 98% 10-year survival rate. Prostate Specific Antigen (PSA) is the primary method of follow-up after definitive radiotherapy (RT), with benign PSA ‘bounce’ occurring in up to 30% of patients, causing uncertainty and anxiety. Distinguishing between PSA bounce and biochemical recurrence (BCR) remains challenging. The Phoenix Criteria is the standard definition of BCR since 2006, defined as PSA nadir + 2 ng/dl, but does not consider patient specific information. Our study aims to explore factors associated with bounce and BCR and develop a predictive tool to differentiate between these two events. Methods: PC patients who received RT at our institution between 2006 and 2022 were identified, including only patients with a PSA increase during follow-up. Exclusion criteria included palliative RT, prostatectomy, or insufficient follow-up information. Patients’ clinical data was collected, and PSA increase was classified as bounce or BCR. Variables were compared using Kruskal Wallis or Chi-square. Multivariate logistic regression was performed in the subset of patients with absolute PSA value >2 (n= 581). Results: Of 1783 patients reviewed due to PSA rise after treatment, 694 met the inclusion criteria. There were 213 (30.7%) with PSA bounce and 481 (69.3%) with recurrence. Prior to meeting the Phoenix Criteria, 20 (4.2%) had biopsy-proven recurrence and 18 (3.7%) imaging-proven recurrence. The median delta PSA was 0.8 ng/mL for bounce vs 5 ng/mL for recurrence (p<0.001). The median time from treatment to event was 14.4 months for bounce vs 45.3 months for BCR (p<0.001). Recurrence was significantly associated (p<0.001) with older age at diagnosis, N1 stage, higher risk category, Gleason score, and higher baseline PSA. In the subgroup of patients without ADT, recurrence was associated with a lower median first PSA post-RT (1.9 vs. 2.4, p=0.046) and a lower median nadir before event (0.6 vs. 1.8, p<0.001). Multivariate logistic regression achieved an accuracy of 0.94, with a sensitivity of 0.83, specificity of 0.98, and an area under the curve (AUC) of 0.97. Conclusions: Our results highlight significant differences in clinical factors between bounce and recurrence in post-RT patients. The multivariate logistic regression differentiated the events with 94% accuracy for patients with a rising PSA value >2 subgroup. We highlight the possible improvement that could be accomplished by personalizing a PSA threshold for recurrence versus the Phoenix definition. This tool offers patients and providers a highly specific tool to differentiate between PSA recurrence and bounce, and may reduce stress and unnecessary testing, or help diagnosis recurrence faster. Next steps include prospective validation of the model to assess the impact on clinical practice costs, patient experience, and cancer outcomes.