UNLABELLED: What's known on the subject? and What does the study add? Previously, it has been reported that PSA may not perform as accurately as PSA density (PSAD) in predicting outcomes after radical prostatectomy among patients with Gleason score 6 prostate cancer. However, there have been few studies comparing the usefulness of PSA and PSAD in predicting upgrading after surgery. Also, most published studies on the prediction of upgrading included significant proportions of subjects who did not undergo contemporary multicore prostate biopsy. Moreover, most studies from major academic centres on the potential usefulness of PSAD as a preoperative predictor of pathological and/or biochemical outcomes after surgery have not included detailed biopsy core-related data. Even when accounting for detailed biopsy core data, this study found that PSAD may be a significantly more accurate preoperative predictor of upgrading than PSA in the current era of extended prostate biopsies. This finding supports the inclusion of PSAD into the risk stratification system for patients with prostate cancer seeking less invasive treatment, such as active surveillance. OBJECTIVE: • To compare the accuracies of prostate-specific antigen (PSA) and PSA density (PSAD) in predicting Gleason score upgrading after radical prostatectomy (RP) in men who have undergone contemporary multicore prostate biopsy and for whom detailed biopsy core data are available. PATIENTS AND METHODS: • We analysed prospectively collected data on 505 patients who were diagnosed with Gleason 6 prostate cancer after multicore (≥ 12 cores) biopsy and who underwent RP without neoadjuvant treatment. • Receiver operating characteristic curves were used to analyse the predictive accuracies of multivariate logistic regression models. RESULTS: • When multivariate models were constructed incorporating either PSA or PSAD along with other upgrading predictors, including biopsy core, both PSA and PSAD were observed to be independent predictors of upgrading in all versions of models (all P < 0.05). • When predictive accuracies of multivariate models including PSA and PSAD were compared, the PSAD model was found to have significantly higher accuracy than the PSA model in three out of four versions of models analysed (model 1, P= 0.048; model 2, P= 0.002; model 3, P= 0.201; model 4, P= 0.044). CONCLUSION: • According to our analysis of prospectively collected data, PSAD may be a significantly more accurate preoperative predictor of upgrading than PSA, even when accounting for detailed biopsy core data in the current era of extended prostate biopsies. • Our findings would support the inclusion of PSAD, rather than PSA, into the risk stratification system for patients seeking less invasive treatment for prostate cancer.
UNLABELLED: What's known on the subject? and What does the study add? Previously, it has been reported that PSA may not perform as accurately as PSA density (PSAD) in predicting outcomes after radical prostatectomy among patients with Gleason score 6 prostate cancer. However, there have been few studies comparing the usefulness of PSA and PSAD in predicting upgrading after surgery. Also, most published studies on the prediction of upgrading included significant proportions of subjects who did not undergo contemporary multicore prostate biopsy. Moreover, most studies from major academic centres on the potential usefulness of PSAD as a preoperative predictor of pathological and/or biochemical outcomes after surgery have not included detailed biopsy core-related data. Even when accounting for detailed biopsy core data, this study found that PSAD may be a significantly more accurate preoperative predictor of upgrading than PSA in the current era of extended prostate biopsies. This finding supports the inclusion of PSAD into the risk stratification system for patients with prostate cancer seeking less invasive treatment, such as active surveillance. OBJECTIVE: • To compare the accuracies of prostate-specific antigen (PSA) and PSA density (PSAD) in predicting Gleason score upgrading after radical prostatectomy (RP) in men who have undergone contemporary multicore prostate biopsy and for whom detailed biopsy core data are available. PATIENTS AND METHODS: • We analysed prospectively collected data on 505 patients who were diagnosed with Gleason 6 prostate cancer after multicore (≥ 12 cores) biopsy and who underwent RP without neoadjuvant treatment. • Receiver operating characteristic curves were used to analyse the predictive accuracies of multivariate logistic regression models. RESULTS: • When multivariate models were constructed incorporating either PSA or PSAD along with other upgrading predictors, including biopsy core, both PSA and PSAD were observed to be independent predictors of upgrading in all versions of models (all P < 0.05). • When predictive accuracies of multivariate models including PSA and PSAD were compared, the PSAD model was found to have significantly higher accuracy than the PSA model in three out of four versions of models analysed (model 1, P= 0.048; model 2, P= 0.002; model 3, P= 0.201; model 4, P= 0.044). CONCLUSION: • According to our analysis of prospectively collected data, PSAD may be a significantly more accurate preoperative predictor of upgrading than PSA, even when accounting for detailed biopsy core data in the current era of extended prostate biopsies. • Our findings would support the inclusion of PSAD, rather than PSA, into the risk stratification system for patients seeking less invasive treatment for prostate cancer.
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Authors: A Pichon; Y Neuzillet; H Botto; J-P Raynaud; C Radulescu; V Molinié; J-M Herve; T Lebret Journal: Prostate Cancer Prostatic Dis Date: 2015-10-06 Impact factor: 5.554
Authors: Matthew Truong; Jon A Slezak; Chee Paul Lin; Viacheslav Iremashvili; Martins Sado; Aria A Razmaria; Glen Leverson; Mark S Soloway; Scott E Eggener; E Jason Abel; Tracy M Downs; David F Jarrard Journal: Cancer Date: 2013-09-04 Impact factor: 6.860
Authors: Isabel Heidegger; Viktor Skradski; Eberhard Steiner; Helmut Klocker; Renate Pichler; Andreas Pircher; Wolfgang Horninger; Jasmin Bektic Journal: PLoS One Date: 2015-02-06 Impact factor: 3.240
Authors: Tamara L Lotan; Filipe Lf Carvalho; Sarah B Peskoe; Jessica L Hicks; Jennifer Good; Helen Fedor; Elizabeth Humphreys; Misop Han; Elizabeth A Platz; Jeremy A Squire; Angelo M De Marzo; David M Berman Journal: Mod Pathol Date: 2014-07-04 Impact factor: 7.842
Authors: Cosimo De Nunzio; Giuseppe Simone; Aldo Brassetti; Riccardo Mastroianni; Devis Collura; Giovanni Muto; Michele Gallucci; Andrea Tubaro Journal: BMC Cancer Date: 2016-07-07 Impact factor: 4.430