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Predicting the pathology results of radical prostatectomy from preoperative information
Author(s) -
Vollmer Robin T.,
Keetch David W.,
Humphrey Peter A.
Publication year - 1998
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/(sici)1097-0142(19981015)83:8<1567::aid-cncr12>3.0.co;2-e
Subject(s) - prostatectomy , medicine , logistic regression , prostate cancer , prostate specific antigen , multivariate statistics , multivariate analysis , stage (stratigraphy) , cancer , urology , surgery , machine learning , computer science , paleontology , biology
BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS 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. Cancer 1998;83:1567‐1580. © 1998 American Cancer Society.