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Comparison of models to predict clinical failure after radical prostatectomy
Author(s) -
Eggener Scott E.,
Vickers Andrew J.,
Serio Angel M.,
Donovan Michael J.,
Khan Faisal M.,
BayerZubek Valentina,
Verbel David,
CordonCardo Carlos,
Reuter Victor E.,
Bianco Fernando J.,
Scardino Peter T.
Publication year - 2009
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/cncr.24016
Subject(s) - nomogram , medicine , prostatectomy , prostate cancer , biochemical recurrence , concordance , proportional hazards model , breakpoint cluster region , cohort , disease , oncology , prostate specific antigen , cancer , pathology , urology , receptor
BACKGROUND: Models are available to accurately predict biochemical disease recurrence (BCR) after radical prostatectomy (RP). Because not all patients experiencing BCR will progress to metastatic disease, it is appealing to determine postoperatively which patients are likely to manifest systemic disease. METHODS: The study cohort consisted of 881 patients undergoing RP between 1985 and 2003. Clinical failure (CF) was defined as metastases, a rising prostate‐specific antigen (PSA) in a castrate state, or death from prostate cancer. The cohort was randomized into training and validation sets. The accuracy of 4 models to predict clinical outcome within 5 years of RP were compared: ‘postoperative BCR nomogram’ and ‘Cox regression CF model’ based on standard clinical and pathologic parameters, and 2 CF ‘systems pathology’ models that integrate clinical and pathologic parameters with quantitative histomorphometric and immunofluorescent biomarker features (‘systems pathology Models 1 and 2’). RESULTS: When applied to the validation set, the concordance index for the postoperative BCR nomogram was 0.85, for the Cox regression CF model 0.84, for systems pathology Model 1 0.81, and for systems pathology Model 2 0.85. CONCLUSIONS: Models predicting either BCR or CF after RP exhibit similarly high levels of accuracy because standard clinical and pathologic variables appear to be the primary determinants of both outcomes. It is possible that introducing current or novel biomarkers found to be uniquely associated with disease progression may further enhance the accuracy of the systems pathology‐based platform. Cancer 2009. © 2009 American Cancer Society.