Nicola Fossati1, Nicolò Maria Buffi2, Alexander Haese3, Carsten Stephan4, Alessandro Larcher2, Thomas McNicholas5, Alexandre de la Taille6, Massimo Freschi7, Giovanni Lughezzani2, Alberto Abrate2, Vittorio Bini8, Joan Palou Redorta9, Markus Graefen3, Giorgio Guazzoni2, Massimo Lazzeri2. 1. Division of Oncology / Unit of Urology, IRCCS Ospedale San Raffaele - Ville Turro, Vita-Salute San Raffaele University, Milan, Italy. Electronic address: nicola.fossati@gmail.com. 2. Division of Oncology / Unit of Urology, IRCCS Ospedale San Raffaele - Ville Turro, Vita-Salute San Raffaele University, Milan, Italy. 3. Martini-Clinic Prostate Cancer Centre, University Clinic Hamburg, Eppendorf Hamburg, Germany. 4. Department of Urology, University Hospital Charité, Berlin, Germany. 5. South Bedfordshire & Hertfordshire Urological Cancer Centre, Lister Hospital, Stevenage, UK. 6. Department of Urology, APHP Mondor Hospital, Créteil, France. 7. Department of Pathology, IRCCS Ospedale San Raffaele, Milan, Italy. 8. Department of Internal Medicine, University of Perugia, Perugia, Italy. 9. Urologic Oncology Section of the Department of Urology and Radiology Department, Fundació Puigvert, Cartagena, Barcelona, Spain.
Abstract
BACKGROUND: Currently available predictive models fail to assist clinical decision making in prostate cancer (PCa) patients who are potential candidates for radical prostatectomy (RP). New biomarkers would be welcome. OBJECTIVE: To test the hypothesis that prostate-specific antigen (PSA) isoform p2PSA and its derivatives, percentage of p2PSA to free PSA (%p2PSA) and the Prostate Health Index (PHI), predict PCa characteristics at final pathology. DESIGN, SETTING, AND PARTICIPANTS: An observational prospective multicentre European study was performed in 489 consecutive PCa patients treated with RP. Total PSA (tPSA), free PSA (fPSA), and p2PSA levels were determined. The %fPSA [(fPSA / tPSA) × 100], %p2PSA [(p2PSA pg/ml) / (fPSA ng/ml × 1000) × 100], and PHI [(p2PSA / fPSA) × √tPSA] were calculated. INTERVENTION: Open or robot-assisted RP. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Logistic regression models were fitted to test the predictors of pT3 stage and/or pathologic Gleason score (GS) ≥7 and to determine their predictive accuracy. The base multivariable model included tPSA, digital rectal examination, biopsy GS, and percentage of positive biopsy cores. Decision curve analysis provided an estimate of the net benefit obtained using p2PSA, %p2PSA, or PHI. RESULTS AND LIMITATIONS: Overall, 344 patients (70%) were affected by pT3 disease or pathologic GS ≥7; pT3 disease and pathologic GS ≥7 were present in 126 patients (26%). At univariable analysis, p2PSA, %p2PSA, and PHI were significant predictors of pT3 disease and/or pathologic GS ≥7 (all p ≤ 0.001). The inclusion of PHI significantly increased the accuracy of the base multivariable model by 2.3% (p=0.003) and 2.4% (p=0.01) for the prediction of pT3 disease and/or pathologic GS ≥7, respectively. However, at decision curve analysis, models including PHI did not show evidence of a greater clinical net benefit. CONCLUSIONS: Both %p2PSA and PHI are significant predictors of unfavourable PCa characteristics at final pathology; however, %p2PSA and PHI did not provide a greater net benefit for clinical decision making. PATIENT SUMMARY: Prostate-specific antigen (PSA) isoform p2PSA and its derivatives, percentage of p2PSA to free PSA and the Prostate Health Index, are associated with adverse characteristics of prostate cancer; however, these biomarkers provided only a slight net benefit for clinical decision making.
BACKGROUND: Currently available predictive models fail to assist clinical decision making in prostate cancer (PCa) patients who are potential candidates for radical prostatectomy (RP). New biomarkers would be welcome. OBJECTIVE: To test the hypothesis that prostate-specific antigen (PSA) isoform p2PSA and its derivatives, percentage of p2PSA to free PSA (%p2PSA) and the Prostate Health Index (PHI), predict PCa characteristics at final pathology. DESIGN, SETTING, AND PARTICIPANTS: An observational prospective multicentre European study was performed in 489 consecutive PCa patients treated with RP. Total PSA (tPSA), free PSA (fPSA), and p2PSA levels were determined. The %fPSA [(fPSA / tPSA) × 100], %p2PSA [(p2PSA pg/ml) / (fPSA ng/ml × 1000) × 100], and PHI [(p2PSA / fPSA) × √tPSA] were calculated. INTERVENTION: Open or robot-assisted RP. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Logistic regression models were fitted to test the predictors of pT3 stage and/or pathologic Gleason score (GS) ≥7 and to determine their predictive accuracy. The base multivariable model included tPSA, digital rectal examination, biopsy GS, and percentage of positive biopsy cores. Decision curve analysis provided an estimate of the net benefit obtained using p2PSA, %p2PSA, or PHI. RESULTS AND LIMITATIONS: Overall, 344 patients (70%) were affected by pT3 disease or pathologic GS ≥7; pT3 disease and pathologic GS ≥7 were present in 126 patients (26%). At univariable analysis, p2PSA, %p2PSA, and PHI were significant predictors of pT3 disease and/or pathologic GS ≥7 (all p ≤ 0.001). The inclusion of PHI significantly increased the accuracy of the base multivariable model by 2.3% (p=0.003) and 2.4% (p=0.01) for the prediction of pT3 disease and/or pathologic GS ≥7, respectively. However, at decision curve analysis, models including PHI did not show evidence of a greater clinical net benefit. CONCLUSIONS: Both %p2PSA and PHI are significant predictors of unfavourable PCa characteristics at final pathology; however, %p2PSA and PHI did not provide a greater net benefit for clinical decision making. PATIENT SUMMARY: Prostate-specific antigen (PSA) isoform p2PSA and its derivatives, percentage of p2PSA to free PSA and the Prostate Health Index, are associated with adverse characteristics of prostate cancer; however, these biomarkers provided only a slight net benefit for clinical decision making.
Authors: Francesco Cantiello; Giorgio Ivan Russo; Antonio Cicione; Matteo Ferro; Sebastiano Cimino; Vincenzo Favilla; Sisto Perdonà; Ottavio De Cobelli; Carlo Magno; Giuseppe Morgia; Rocco Damiano Journal: World J Urol Date: 2015-07-21 Impact factor: 4.226
Authors: Anssi Auvinen; Antti Rannikko; Kimmo Taari; Paula Kujala; Tuomas Mirtti; Anu Kenttämies; Irina Rinta-Kiikka; Terho Lehtimäki; Niku Oksala; Kim Pettersson; Teuvo L Tammela Journal: Eur J Epidemiol Date: 2017-07-31 Impact factor: 8.082