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Background Risk stratification tools are useful to optimize treatment decisions in each individual patient, reducing both underand overtreatment. Currently, several such tools are available to estimate disease aggressiveness in patients with localized prostate cancer (PCa). These include nomograms (e.g., Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram), tiered classification systems (e.g., D’Amico, National Comprehensive Cancer Network (NCCN)), and risk assessment scoring systems (e.g., the cancer of the prostate risk assessment (CAPRA) score) [1]. Preoperative parameters such as biopsy Gleason score (or ISUP grade), initial PSA at time of biopsy, and clinical T-stage at digital rectal examination are used in these tools. The D’Amico classification, an easy-to-use three-tier classification system based on these three preoperative parameters, is adopted in daily clinical practice and endorsed by several international guidelines, such as the European Association of Urology (EAU) prostate cancer guidelines [2]. However, most of these risk stratification tools are designed to predict the risk of PSA recurrence after primary treatment rather than survival and do not take into account individual competing risk mortality in terms of age and comorbidity. PREDICT Prostate, a novel promising tool The PREDICT Prostate tool, on the other hand, is a multivariable prognostic model that provides individualized cancer-specific and overall long-term survival estimates in localized PCa patients [3]. In addition to the use of routinely available preoperative clinical-pathological variables such as PSA, biopsy Gleason score (ISUP grade group), and clinical T-stage, the PREDICT Prostate tool also includes the impact of patient characteristics (age and comorbidity status) and radical treatment (radical prostatectomy or radiotherapy) on survival. Thurtle et al. [4] performed an external validation of their previously published PREDICT Prostate model. Applied to the large Swedish PCBaSe cohort, the tool was able to discriminate patients who faced PCa-specific mortality and outperformed other widely used models such as the CAPRA and the three-tier EAU classification. It was proven to generally have high c-indices for all-cause and PCa-specific mortality, and the model calibration was good and remained accurate within the treatment subgroups. However, some issues need to be emphasized before using the tool in daily clinical practice. First, the two cohorts (original United Kingdom (UK) and Swedish PCBaSe cohort) are generally very similar in epidemiological characteristics. Yet, more than half (53%) of the patients in the PCBaSe had low-grade disease (ISUP grade group 1) compared to 32% in the original UK cohort. This high number of low-risk patients may affect the discriminatory power of the tool as these men are

bmc medicine

PREDICT Prostate, a useful tool in men with low- and intermediate-risk prostate cancer who are hesitant between conservative management and active treatment