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J Urol. 1996 Oct;156(4):1375-80.

An algorithm for predicting nonorgan confined prostate cancer using the results obtained from sextant core biopsies with prostate specific antigen level.

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  • 1Crittenton Hospital, Rochester Hills, Michigan, USA.

Abstract

PURPOSE:

We determined the enhanced ability to predict nonorgan confined prostate cancer using several histopathological and quantitative nuclear imaging parameters combined with serum prostate specific antigen (PSA).

MATERIALS AND METHODS:

Several independent pathological and quantitative image analysis variables obtained from sextant biopsy specimens, as well as preoperative PSA were used. The study population included 210 patients with pathologically staged disease (192 with PSA). All variables were examined by univariate and multivariate logistic regression analyses to assess ability to predict disease organ confinement status.

RESULTS:

Univariate logistic regression analysis demonstrated that, in decreasing order, quantitative nuclear grade, preoperative PSA, total percent tumor involvement, number of positive sextant cores, preoperative Gleason score and involvement of more than 5% of a base and/or apex biopsy were significant (p < or = 0.006) for prediction of disease organ confinement status. Backward stepwise logistic regression was applied to these univariately significant variables, including deoxyribonucleic acid ploidy, to calculate a multivariate model for prediction of disease organ confinement status. This algorithm had a sensitivity of 85.7%, specificity 71.3%, positive predictive value 72.9%, negative predictive value 84.7% and area under the receiver operating characteristic curve 85.9%.

CONCLUSIONS:

Information from pathological study of sextant prostate biopsies, preoperative PSA blood test and a new image analysis variable termed quantitative nuclear grade can be combined to create a multivariate algorithm that can predict more accurately nonorgan confined prostate cancer compared to previously reported methods.

PMID:
8808875
[PubMed - indexed for MEDLINE]
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