Robert W Foley1,2, Robert M Maweni3, Laura Gorman4, Keefe Murphy5,6, Dara J Lundon7,4,8, Garrett Durkan9,10, Richard Power11, Frank O'Brien12, Kieran J O'Malley8, David J Galvin4,8,13, T Brendan Murphy5,6, R William Watson7,4. 1. UCD School of Medicine, University College Dublin, Dublin, Ireland. robert.foley.2@ucdconnect.ie. 2. UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland. robert.foley.2@ucdconnect.ie. 3. Croydon NHS Trust, Croydon University Hospital, London, UK. 4. UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland. 5. UCD School of Mathematical Sciences, University College Dublin, Dublin, Ireland. 6. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland. 7. UCD School of Medicine, University College Dublin, Dublin, Ireland. 8. Department of Urology, Mater Misericordiae University Hospital, Dublin, Ireland. 9. Department of Urology, University Hospital Galway, Galway, Ireland. 10. Department of Urology, University Hospital Limerick, Limerick, Ireland. 11. Department of Urology, Beaumont Hospital, Dublin, Ireland. 12. Department of Urology, University Hospital Waterford, Waterford, Ireland. 13. Department of Urology, St. Vincent's University Hospital, Dublin, Ireland.
Abstract
OBJECTIVE: To analyse the performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) and two iterations of the European Randomised Study of Screening for Prostate Cancer (ERSPC) Risk Calculator, one of which incorporates prostate volume (ERSPC-RC) and the other of which incorporates prostate volume and the prostate health index (PHI) in a referral population (ERSPC-PHI). PATIENTS AND METHODS: The risk of prostate cancer (PCa) and significant PCa (Gleason score ≥7) in 2001 patients from six tertiary referral centres was calculated according to the PCPT-RC and ERSPC-RC formulae. The calculators' predictions were analysed using the area under the receiver-operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test for goodness of fit and decision-curve analysis. In a subset of 222 patients for whom the PHI score was available, each patient's risk was calculated as per the ERSPC-RC and ERSPC-PHI risk calculators. RESULTS: The ERSPC-RC outperformed the PCPT-RC in the prediction of PCa, with an AUC of 0.71 compared with 0.64, and also outperformed the PCPT-RC in the prediction of significant PCa (P<0.001), with an AUC of 0.74 compared with 0.69. The ERSPC-RC was found to have improved calibration in this cohort and was associated with a greater net benefit on decision-curve analysis for both PCa and significant PCa. The performance of the ERSPC-RC was further improved through the addition of the PHI score in a subset of 222 patients. The AUCs of the ERSPC-PHI were 0.76 and 0.78 for PCa and significant PCa prediction, respectively, in comparison with AUC values of 0.72 in the prediction of both PCa and significant PCa for the ERSPC-RC (P = 0.12 and P = 0.04, respectively). The ERSPC-PHI risk calculator was well calibrated in this cohort and had an increase in net benefit over that of the ERSPC-RC. CONCLUSIONS: The performance of the risk calculators in the present cohort shows that the ERSPC-RC is a superior tool in the prediction of PCa; however the performance of the ERSPC-RC in this population does not yet warrant its use in clinical practice. The incorporation of the PHI score into the ERSPC-PHI risk calculator allowed each patient's risk to be more accurately quantified. Individual patient risk calculation using the ERSPC-PHI risk calculator can be undertaken in order to allow a systematic approach to patient risk stratification and to aid in the diagnosis of PCa.
RCT Entities:
OBJECTIVE: To analyse the performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) and two iterations of the European Randomised Study of Screening for Prostate Cancer (ERSPC) Risk Calculator, one of which incorporates prostate volume (ERSPC-RC) and the other of which incorporates prostate volume and the prostate health index (PHI) in a referral population (ERSPC-PHI). PATIENTS AND METHODS: The risk of prostate cancer (PCa) and significant PCa (Gleason score ≥7) in 2001 patients from six tertiary referral centres was calculated according to the PCPT-RC and ERSPC-RC formulae. The calculators' predictions were analysed using the area under the receiver-operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test for goodness of fit and decision-curve analysis. In a subset of 222 patients for whom the PHI score was available, each patient's risk was calculated as per the ERSPC-RC and ERSPC-PHI risk calculators. RESULTS: The ERSPC-RC outperformed the PCPT-RC in the prediction of PCa, with an AUC of 0.71 compared with 0.64, and also outperformed the PCPT-RC in the prediction of significant PCa (P<0.001), with an AUC of 0.74 compared with 0.69. The ERSPC-RC was found to have improved calibration in this cohort and was associated with a greater net benefit on decision-curve analysis for both PCa and significant PCa. The performance of the ERSPC-RC was further improved through the addition of the PHI score in a subset of 222 patients. The AUCs of the ERSPC-PHI were 0.76 and 0.78 for PCa and significant PCa prediction, respectively, in comparison with AUC values of 0.72 in the prediction of both PCa and significant PCa for the ERSPC-RC (P = 0.12 and P = 0.04, respectively). The ERSPC-PHI risk calculator was well calibrated in this cohort and had an increase in net benefit over that of the ERSPC-RC. CONCLUSIONS: The performance of the risk calculators in the present cohort shows that the ERSPC-RC is a superior tool in the prediction of PCa; however the performance of the ERSPC-RC in this population does not yet warrant its use in clinical practice. The incorporation of the PHI score into the ERSPC-PHI risk calculator allowed each patient's risk to be more accurately quantified. Individual patient risk calculation using the ERSPC-PHI risk calculator can be undertaken in order to allow a systematic approach to patient risk stratification and to aid in the diagnosis of PCa.
Authors: Frank-Jan H Drost; Daniël F Osses; Daan Nieboer; Ewout W Steyerberg; Chris H Bangma; Monique J Roobol; Ivo G Schoots Journal: Cochrane Database Syst Rev Date: 2019-04-25
Authors: Ricardo A Rendon; Ross J Mason; Karim Marzouk; Antonio Finelli; Fred Saad; Alan So; Phillipe Violette; Rodney H Breau Journal: Can Urol Assoc J Date: 2017-10 Impact factor: 1.862
Authors: M Maruf; M Fascelli; A K George; M M Siddiqui; M Kongnyuy; J M DiBianco; A Muthigi; S Valayil; A Sidana; T P Frye; A Kilchevsky; P L Choyke; B Turkbey; B J Wood; P A Pinto Journal: Prostate Cancer Prostatic Dis Date: 2017-02-21 Impact factor: 5.554