Donna P Ankerst1, Johanna Straubinger2, Katharina Selig2, Lourdes Guerrios3, Amanda De Hoedt4, Javier Hernandez5, Michael A Liss5, Robin J Leach5, Stephen J Freedland6, Michael W Kattan7, Robert Nam8, Alexander Haese9, Francesco Montorsi10, Stephen A Boorjian11, Matthew R Cooperberg12, Cedric Poyet13, Emily Vertosick14, Andrew J Vickers14. 1. Department of Mathematics, Technical University of Munich, Garching, Munich, Germany; Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. Electronic address: ankerst@tum.de. 2. Department of Mathematics, Technical University of Munich, Garching, Munich, Germany. 3. Department of Surgery, Urology Section, Veterans Affairs Caribbean Healthcare System, San Juan, Puerto Rico. 4. Section of Urology, Durham Veterans Administration Medical Center, Durham, NC, USA. 5. Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. 6. Section of Urology, Durham Veterans Administration Medical Center, Durham, NC, USA; Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 7. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA. 8. Division of Urology, Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada; Institute of Health Policy, Management, & Evaluation, University of Toronto, Toronto, Ontario, Canada. 9. Martini-Clinic Prostate Cancer Center, University Clinic Eppendorf, Hamburg, Germany. 10. Division of Oncology/Unit of Urology, URI, IRCCS Hospital San Raffaele, Milano, Italy; Department of Medicine, Vita-Salute San Raffaele University, Milano, Italy. 11. Department of Urology, Mayo Clinic, Rochester, MN, USA. 12. Departments of Urology and Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA. 13. Department of Urology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. 14. Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Abstract
BACKGROUND: Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems. OBJECTIVE: We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool. DESIGN, SETTING, AND PARTICIPANTS: We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts. RESULTS AND LIMITATIONS: Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies. CONCLUSIONS: The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome. PATIENT SUMMARY: A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making.
BACKGROUND:Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems. OBJECTIVE: We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool. DESIGN, SETTING, AND PARTICIPANTS: We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts. RESULTS AND LIMITATIONS: Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies. CONCLUSIONS: The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome. PATIENT SUMMARY: A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making.
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