PURPOSE: We developed a model to predict the side specific probability of extracapsular extension (ECE) in radical prostatectomy (RP) specimens based on the clinical features of the cancer. MATERIALS AND METHODS: We studied 763 patients with clinical stage T1c-T3 prostate cancer who were diagnosed by systematic needle biopsy and subsequently treated with RP. Candidate predictor variables associated with ECE were clinical T stage, the highest Gleason sum in any core, percent positive cores, percent cancer in the cores from each side and serum prostate specific antigen (PSA). Receiver operating characteristic (ROC) analyses were performed to assess the predictive value of each variable alone and in combination. We constructed and internally validated nomograms to predict the side specific probability of ECE based on logistic regression analysis. RESULTS: Overall 30% of the patients and 17% of 1,526 prostate lobes (left or right) had ECE. The areas under the ROC curves (AUC) of the standard features in predicting side specific probability of ECE were 0.627 for PSA, 0.695 for clinical T stage on each side and 0.727 for Gleason sum on each side. When these features were combined predictive accuracy increased to 0.788. The highest value (0.806) was achieved by adding the percent positive cores and the percent cancer in the biopsy specimen to the standard features. The resulting nomograms were internally validated and had excellent calibration and discrimination accuracy. CONCLUSIONS: Standard clinical features of prostate cancer in each lobe-PSA, palpable induration and biopsy Gleason sum-can be used to predict the side specific probability of ECE in RP specimens. The predictive accuracy is increased by adding information from systematic biopsy results. The predictive nomograms are sufficiently accurate for use in clinical practice in decisions such as wide versus close dissection of the cavernous nerves from the prostate.
PURPOSE: We developed a model to predict the side specific probability of extracapsular extension (ECE) in radical prostatectomy (RP) specimens based on the clinical features of the cancer. MATERIALS AND METHODS: We studied 763 patients with clinical stage T1c-T3 prostate cancer who were diagnosed by systematic needle biopsy and subsequently treated with RP. Candidate predictor variables associated with ECE were clinical T stage, the highest Gleason sum in any core, percent positive cores, percent cancer in the cores from each side and serum prostate specific antigen (PSA). Receiver operating characteristic (ROC) analyses were performed to assess the predictive value of each variable alone and in combination. We constructed and internally validated nomograms to predict the side specific probability of ECE based on logistic regression analysis. RESULTS: Overall 30% of the patients and 17% of 1,526 prostate lobes (left or right) had ECE. The areas under the ROC curves (AUC) of the standard features in predicting side specific probability of ECE were 0.627 for PSA, 0.695 for clinical T stage on each side and 0.727 for Gleason sum on each side. When these features were combined predictive accuracy increased to 0.788. The highest value (0.806) was achieved by adding the percent positive cores and the percent cancer in the biopsy specimen to the standard features. The resulting nomograms were internally validated and had excellent calibration and discrimination accuracy. CONCLUSIONS: Standard clinical features of prostate cancer in each lobe-PSA, palpable induration and biopsy Gleason sum-can be used to predict the side specific probability of ECE in RP specimens. The predictive accuracy is increased by adding information from systematic biopsy results. The predictive nomograms are sufficiently accurate for use in clinical practice in decisions such as wide versus close dissection of the cavernous nerves from the prostate.
Authors: Giovanni Lughezzani; Alberto Briganti; Pierre I Karakiewicz; Michael W Kattan; Francesco Montorsi; Shahrokh F Shariat; Andrew J Vickers Journal: Eur Urol Date: 2010-08-06 Impact factor: 20.096
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Authors: Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino Journal: Future Oncol Date: 2009-12 Impact factor: 3.404