Promise Criteria Accurately Predicts Survival in Prostate Cancer - EMJ

PROMISE Criteria Accurately Predicts Survival in Prostate Cancer

1 Mins
Urology

RESEARCHERS have validated the prognostic value of using prostate-specific membrane antigen (PSMA)-PET findings, according to the PROMISE criteria (PPP), in predicting overall survival (OS) in patients in varying staging of prostate cancer.  

The study included 2,414 adult patients with biopsy-proven prostate cancer who underwent PSMA-PET imaging at Essen University Hospital and other German institutions from 2014 to 2021. Patients were divided into development (n=1110) and validation cohorts (n=502), with an external validation cohort (n=802) from University Hospitals in Münster, Freiburg, and Dresden. The study identified several predictors of OS, such as locoregional lymph node metastases, distant metastases, tumor volume, and tumour SUVmean in the quantitative nomogram. For the visual nomogram, the number of distant metastases and total tumour lesion count were predictive factors. The accuracy of these nomograms was measured using the C-index, showing 0.80 and 0.77 in internal and external quantitative PPP nomograms, and 0.78 and 0.77 in the visual nomograms, respectively. Moreover, the PPP nomograms outperformed established risk models, such as STARCAP and European Association of Urology (EAU) scores, with significantly higher accuracy at various stages of disease progression.  

In conclusion, it was demonstrated that PSMA-PET-based PROMISE nomograms are able to better predict OS in patients with prostate cancer than conventional risk tools. The study suggests these nomograms provide more precise predictions of survival outcomes across various stages of prostate cancer, from initial diagnosis to recurrence. Further validation of these tools with long-term follow-up data, including a 10-year target duration, is ongoing.  

Katrina Thornber, EMJ 

Reference  

Fendler WP et al. PSMA-PET and PROMISE re-define stage and risk in patients with prostate cancer. Abstract 1609P. ESMO Congress, 13-17 September, 2024.  

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