Risk stratification tools in prostate cancer, where do we stand?

Abstract

In an attempt to predict outcomes and individualize care for prostate cancer (PCa) patients, parallels are drawn between a patient’s current health status and similar cases in the past. To adopt this in clinical practice, risk stratification tools are frequently used. These are, in essence, equations relating multiple factors for a particular individual to the probability or risk for future occurrence of particular outcomes in a certain time period. The terms prognostic and predictive are frequently misunderstood, especially in the field of biomarkers and precision medicine. When a possible interaction (e.g., an intervention) is taken into account, a measurement can be called predictive because it is associated with the impact of a specific therapy. A control group is always needed to evaluate the interaction between treatment benefit and a biomarker or clinical factor (1). Prognostic factors on the other hand provide information on outcomes regardless of any therapeutic intervention (1,2). A biomarker panel can, for example, assign a tumor’s aggressiveness regardless of the treatment that will be provided, where a predictive test could identify which patients will benefit from radiotherapy after biochemical recurrence post-surgery. The main goal of risk stratification tools is to answer how lethal prostate cancer is, with and without therapy, and how different therapies influence survival outcomes. Most models use similar predictor variables as originally used by D’Amico to differentiate between risk groups: prostate-specific antigen (PSA) level, clinical T-stage, and biopsy Gleason score (3). Adding more variables generally provides more granularity but also increases complexity and impairs usability, as seen in more recent risk scores and nomograms (4-7). Due to the abundancy of available pre-treatment tools, it can be hard for clinicians and patients to select the appropriate instrument. Zelic et al. recently reported on a head-to-head comparison of nine widely used pretreatment risk stratification tools predicting PCa death and measured their individual performance within the prospectively collected Swedish prostate cancer database (PCBaSe) (8). They found that the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, Cancer of the Prostate Risk Assessment (CAPRA) score and Cambridge Prognostic Groups (CPG) showed better performance in predicting prostate cancer death than D’Amico risk stratification system and derived tool. However, a great effort is done by the team of Zelic, there are some additional key factors to consider when comparing risk models.