Prediction of biochemical recurrence after radical prostatectomy from primary tumour characteristics

Objectives To construct and externally calibrate a predictive model for early biochemical recurrence (BCR) after radical prostatectomy (RP) incorporating clinical and modern imaging characteristics of the primary tumour. Patients and Methods Patients who underwent RP following multiparametric magnetic resonance imaging, prostate biopsy and prostate‐specific membrane antigen‐positron emission tomography/computed tomography (PSMA‐PET/CT), from two centres in Australia and the Netherlands. The primary outcome was biochemical recurrence‐free survival (BRFS), where BCR was defined as a rising PSA level of ≥0.2 ng/mL or initiation of postoperative treatment per clinician discretion. Proportional hazards models to predict time to event were developed in the Australian sample using relevant pre‐ and post‐surgical parameters and primary tumour maximum standardised uptake value (SUV max ) on diagnostic PSMA‐PET/CT. Calibration was assessed in an external dataset from the Netherlands with the same inclusion criteria. Results Data from 846 patients were used to develop the models. Tumour SUV max was associated with worse predicted 3‐year BRFS for both pre‐ and post‐surgical models. SUV max change from 4 to 16 lessened the predicted 3‐year BRFS from 66% to 42% for a patient aged 65 years with typical pre‐surgical parameters (PSA level 8 ng/mL, Prostate Imaging‐Reporting and Data System score 4/5 and biopsy Gleason score ≥4 + 5). Considering post‐surgical variables, a patient with the same age and PSA level but pathological stage pT3a, RP Gleason score ≥4 + 5 and negative margins, SUV max change from 4 to 16 lessened the predicted 3‐year BRFS from 76% to 61%. Calibration on an external sample ( n = 464) showed reasonable performance; however, a tendency to overestimate survival in patients with good prognostic factors was observed. Conclusion Tumour SUV max on diagnostic PSMA‐PET/CT has utility additional to commonly recognised variables for prediction of BRFS after RP.