August 2, 2016
Over 80% of the nearly 1 million men diagnosed with prostate cancer annually worldwide present with localised or locally advanced non-metastatic disease. Risk stratification is the cornerstone for clinical decision making and treatment selection for these men. The most widely applied stratification systems use presenting prostate-specific antigen (PSA) concentration, biopsy Gleason grade, and clinical stage to classify patients as low, intermediate, or high risk. There is, however, significant heterogeneity in outcomes within these standard groupings. The International Society of Urological Pathology (ISUP) has recently adopted a prognosis-based pathological classification that has yet to be included within a risk stratification system. Here we developed and tested a new stratification system based on the number of individual risk factors and incorporating the new ISUP prognostic score.
Methods and Findings
Diagnostic clinicopathological data from 10,139 men with non-metastatic prostate cancer were available for this study from the Public Health England National Cancer Registration Service Eastern Office. This cohort was divided into a training set (n = 6,026; 1,557 total deaths, with 462 from prostate cancer) and a testing set (n = 4,113; 1,053 total deaths, with 327 from prostate cancer). The median follow-up was 6.9 y, and the primary outcome measure was prostate-cancer-specific mortality (PCSM). An external validation cohort (n = 1,706) was also used. Patients were first categorised as low, intermediate, or high risk using the current three-stratum stratification system endorsed by the National Institute for Health and Care Excellence (NICE) guidelines. The variables used to define the groups (PSA concentration, Gleason grading, and clinical stage) were then used to sub-stratify within each risk category by testing the individual and then combined number of risk factors. In addition, we incorporated the new ISUP prognostic score as a discriminator. Using this approach, a new five-stratum risk stratification system was produced, and its prognostic power was compared against the current system, with PCSM as the outcome. The results were analysed using a Cox hazards model, the log-rank test, Kaplan-Meier curves, competing-risks regression, and concordance indices. In the training set, the new risk stratification system identified distinct subgroups with different risks of PCSM in pair-wise comparison (p < 0.0001). Specifically, the new classification identified a very low-risk group (Group 1), a subgroup of intermediate-risk cancers with a low PCSM risk (Group 2, hazard ratio [HR] 1.62 [95% CI 0.96–2.75]), and a subgroup of intermediate-risk cancers with an increased PCSM risk (Group 3, HR 3.35 [95% CI 2.04–5.49]) (p < 0.0001). High-risk cancers were also sub-classified by the new system into subgroups with lower and higher PCSM risk: Group 4 (HR 5.03 [95% CI 3.25–7.80]) and Group 5 (HR 17.28 [95% CI 11.2–26.67]) (p < 0.0001), respectively. These results were recapitulated in the testing set and remained robust after inclusion of competing risks. In comparison to the current risk stratification system, the new system demonstrated improved prognostic performance, with a concordance index of 0.75 (95% CI 0.72–0.77) versus 0.69 (95% CI 0.66–0.71) (p < 0.0001). In an external cohort, the new system achieved a concordance index of 0.79 (95% CI 0.75–0.84) for predicting PCSM versus 0.66 (95% CI 0.63–0.69) (p < 0.0001) for the current NICE risk stratification system. The main limitations of the study were that it was registry based and that follow-up was relatively short.
A novel and simple five-stratum risk stratification system outperforms the standard three-stratum risk stratification system in predicting the risk of PCSM at diagnosis in men with primary non-metastatic prostate cancer, even when accounting for competing risks. This model also allows delineation of new clinically relevant subgroups of men who might potentially receive more appropriate therapy for their disease. Future research will seek to validate our results in external datasets and will explore the value of including additional variables in the system in order in improve prognostic performance.