Predicting the pathology results of radical prostatectomy from preoperative information: a validation study

There are now over 13 published models for predicting the outcomes of radical prostatectomy using preoperative information. Because their ability to predict the pathology of the prostatectomy is key in deciding who benefits the most from this surgery, it is important to know how well these models work for new data.

The patients in this study were 100 men diagnosed with prostate carcinoma in the prostate specific antigen (PSA)-based screening program at Washington University Medical Center. To test the models, the authors used preoperative information and the published algorithms to predict postoperative pathology outcomes. Statistical methods included plots of predicted probability against observed probability, boxplots of predicted probability against observed outcomes, logistic regression, and linear regression.

Although none of the published models predicted the outcomes of radical prostatectomy perfectly, those that predicted tumor volume performed best, and in general those that were multivariate also performed best. Nevertheless, the ability of any of these models to discriminate binary outcomes was not very great.

The results of this study suggest that preoperative variables based on serum PSA and the results of needle biopsies can be used in multivariate models to predict tumor volume, but these models need to be improved. Predicting locally advanced tumor stage is likely to be more difficult and may require information beyond what needle biopsies can provide.