Clinical stage provides useful prognostic information even after pathological stage is known for prostate cancer in the PSA era. - Maxine M Chen (Public Library of Science (PLoS), 2020)
Background Pathological and clinical stage are associated with prostate cancer-specific survival after prostatectomy. With PSA screening, the post-surgery prognostic utility of clinical stage is debatable in studies seeking to identify new biomarkers. Few studies have investigated clinical stage and lethal prostate cancer association after accounting for pathological stage. We hypothesize that clinical stage provides prognostic information beyond pathological stage in the PSA era. Methods Cox regression models tested associations between clinical and pathological stage and lethal prostate cancer among 3,064 participants from the Health Professionals Follow-Up Study and Physicians' Health Study (HPFS/PHS) who underwent prostatectomy. Likelihood ratio tests and c-statistics were used to assess the models' prognostic utility. Equivalent analyses were performed in 16,134 men who underwent prostatectomy at Johns Hopkins. Results Independently, clinical and pathological stage were associated (p<0.0001 for both) with rate of lethal prostate cancer in HPFS/PHS. The model with clinical and pathological stage fit significantly better than the model with only pathological stage in all men (p = 0.01) and in men diagnosed during the PSA era (p = 0.04). The mutually adjusted model also improved discriminatory ability. In the Johns Hopkins cohort, the model with clinical and pathological stage improved discriminatory ability and fit significantly better overall (p<0.0001) and in the PSA era (p<0.0001). Conclusions Despite stage migration resulting from widespread PSA screening, clinical stage remains associated with progression to lethal prostate cancer independent of pathological stage. Future studies evaluating associations between new factors and poor outcome following prostatectomy should consider including both clinical and pathological stages since the data is already available.