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Eur Urol. 2018 Aug;74(2):197-203. doi: 10.1016/j.eururo.2018.05.003. Epub 2018 May 16.

A Contemporary Prostate Biopsy Risk Calculator Based on Multiple Heterogeneous Cohorts.

Author information

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Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems.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.

KEYWORDS:

Digital rectal exam; Family history; High-grade disease; Prostate cancer; Prostate-specific antigen; Risk prediction

PMID:
29778349
PMCID:
PMC6082177
[Available on 2019-08-01]
DOI:
10.1016/j.eururo.2018.05.003

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