Jereczek-Fossa, Barbara Alicja

Purpose: To evaluate the impact of PSMA PET versus conventional imaging for staging, and the effect of dose escalation (DE) to metastatic lymph nodes in cN1 prostate cancer patients treated with EBRT and ADT. Methods: A retrospective multicenter analysis included 197 propensity score-matched patients (77 staged with PSMA PET, 120 with conventional imaging) treated with EBRT and ADT, with or without DE to lymph node metastases. Matching criteria were T stage, nodal involvement, imaging modality, Gleason score, and ADT duration. Primary endpoint was metastasis-free survival (MFS); secondary endpoints included overall survival (OS), locoregional control (LRC), and toxicity. Results: Median follow-up was 42 months (IQR: 25–59). Median EQD2 (alpha/beta=1.5) to elective nodes was 46 Gy, and to metastatic nodes, 66 Gy (PSMA PET) and 63 Gy (conventional imaging). The 3-year MFS was 80.5% (95% CI: 71.3–90.8%) for PSMA PET and 77.2% (95% CI: 69.5–85.8%) for conventional imaging; 5-year MFS was 59.3% versus 65.8% ( P =0.57). OS at 5 years was 82.2% (PSMA PET) and 77.4% (conventional imaging) ( P =0.72). On multivariable analysis, ISUP grade group 3–5 predicted earlier metastasis (HR: 4.03, 95% CI: 1.61–10.06; P =0.003); higher prostate dose (EQD2 ≥113 Gy) improved MFS (HR: 0.53, 95% CI: 0.30–0.95; P =0.033); long-term ADT (≥24 mo) improved OS (HR: 0.38, 95% CI: 0.19–0.78; P =0.009). No significant difference in toxicity was observed between groups. Conclusions: PSMA PET staging and nodal dose escalation >60 Gy were safe but did not improve MFS, OS, or LRC. ISUP grade group 3–5 and higher prostate dose significantly influenced outcomes.

160 Background: To our knowledge, no tools are available to guide care in oligometastatic prostate cancer (omPC). Here, we leveraged individual patient-level data pooled from 8 randomized trials in the X-Met collaboration to construct CEREBRO, a tool for predicting patient-specific responses to various treatment scenarios. Methods: Patients with omPC from the STOMP, ORIOLE, SABR COMET, EXTEND continuous ADT, EXTEND intermittent ADT, ARTO, RADIOSA, and RAVENS trials were included. 6 patients (1%) with missing data were excluded. Treatment scenarios were defined as active surveillance (AS), metastasis-directed therapy (MDT), systemic therapy (ST), or MDT+ST. To account for varying baseline hazards, Weibull models were fit for overall survival (OS); progression-free survival (PFS); and castration-resistance free survival (CRFS) for patients with hormone-sensitive disease. Endpoint definitions were harmonized per X-Met framework. Results: Of 600 patients included (treatment scenarios: AS=50; MDT=153; ST=172; MDT+ST=225), 405 (67%) had hormone-sensitive omPC. With median follow-up of 38 months, median PFS, CRFS, and OS of all patients were 18, 70, and 84 months. In order of relative contribution, inputs for predicting outcomes were: treatment scenario, stage, prostate-specific antigen annotated by ADT status, metachronous vs synchronous presentation, imaging type, number of metastases, hormone sensitivity, and age. The model for each endpoint showed robust fit, calibration, superiority against the null model (per the likelihood ratio test), and risk stratification (Table). Median PFS times for model-defined low-, intermediate-, and high-risk strata were 46 months, 18 months, and 7 months ( p <0.0001). In addition, the PFS and OS models also showed strong discrimination via c index and a large interval of risk probabilities with additive clinical utility per decision-curve analysis (Table). A public webpage will be made available to facilitate prospective trial stratification, patient counseling for patient-specific outcome predictions for each potential treatment scenario, and forecasts comparing outcomes between treatment scenarios. Conclusions: CEREBRO is a high-performance tool, trained on robust prospective data from 8 randomized trials with routinely collected clinical variables, for predicting risk and treatment outcomes in omPC. Given the uncertainties in optimal timing of MDT and combination with ST, this tool has been built to facilitate current decision making. External validation is planned. Model summary. Endpoint C index (95% CI) Calibration Likelihood ratio p Risk strata p Net clinical utility observed between thresholds PFS 0.72 (0.70-0.75) 1.04 <0.0001 <0.0001 0.2-1.0 CRFS 0.63 (0.60-0.70) 1.01 0.02 0.004 0.2-0.5 OS 0.81 (0.76-0.86) 1.01 <0.0001 <0.0001 0.05-1.0