Update of the MSKCC nomogram for metastatic progression and its role in active surveillance: the Italian TPCP cohort

Background – Prognostic models are crucial for prostate cancer (PCa) treatment decision making at the time of diagnosis, particularly for distinguishing active surveillance (AS) candidates from those requiring curative treatment. While several models exist, their ability to predict metastatic disease—the primary driver of PCa mortality—remains underexplored. Methods – We analysed the Turin Prostate Cancer Prognostication cohort, which includes 891 unselected PCa patients diagnosed between 2008 and 2013 in Turin, Italy. Three widely used prognostic models—D’Amico, CAPRA, and MSKCC—were updated and compared based on optimism-corrected discrimination and overall prediction error for metastatic PCa (mPCa) within five years of diagnosis, accounting for competing risks. Overall survival was also assessed. Additionally, we investigated whether replacing standard AS eligibility criteria with nomogram-based risk thresholds could better identify patients at low risk of metastasis, maximizing AS uptake while minimising metastatic risk. Results – The MSKCC nomogram (optimism-corrected AUCt: 0.81; scaled Brier score: 0.15) outperforming the CAPRA score (AUCt: 0.77; Brier score: 0.11) and the D’Amico classification (AUCt 0.64; Brier score: 0.03) in predicting mPCa. The same ranking was observed for overall mortality prediction. When 95th percentile of MSKCC’s predicted probabilities among patients selected for six different AS protocols was used as a threshold, the proportion of potentially eligible patients increased from 7.8% when UCSF criterion was used to 57.0% without substantially increasing metastatic risk (observed 5-year risk: 1.7%). Conclusions – The MSKCC nomogram outperformed other models in predicting mPCa and overall mortality. Implementing risk-based AS eligibility thresholds derived from MSKCC could enhance patient selection while facilitating shared decision-making between patients and clinicians.