Literature DB >> 17222622

Development and external validation of an extended repeat biopsy nomogram.

Felix K-H Chun1, Alberto Briganti, Markus Graefen, Christopher Porter, Francesco Montorsi, Alexander Haese, Vincenzo Scattoni, Lester Borden, Thomas Steuber, Andrea Salonia, Thorsten Schlomm, Kalyan Latchemsetty, Jochen Walz, Jason Kim, Christian Eichelberg, Eike Currlin, Sascha A Ahyai, Andreas Erbersdobler, Luc Valiquette, Hans Heinzer, Patrizio Rigatti, Hartwig Huland, Pierre I Karakiewicz.   

Abstract

PURPOSE: We hypothesized that the outcome of repeat biopsy could be accurately predicted. We tested this hypothesis in a contemporary cohort from 3 centers.
MATERIALS AND METHODS: The principal cohort of 1,082 men from Hamburg, Germany was used for nomogram development as well as for internal 200 bootstrap validation in 721 and external validation in 361. Two additional external validation cohorts, including 87 men from Milan, Italy and 142 from Seattle, Washington, were also used. Predictors of prostate cancer on repeat biopsy were patient age, digital rectal examination, prostate specific antigen, percent free prostate specific antigen, number of previous negative biopsy sessions and sampling density. Multivariate logistic regression models were used to develop the nomograms.
RESULTS: The mean number of previous negative biopsies was 1.5 (range 1 to 6) and the mean number of cores at final repeat biopsy was 11.1 (range 10 to 24). Of the men 370 (30.2%) had prostate cancer. On multivariate analyses all predictors were statistically significant (p < or =0.028). After internal validation the nomogram was 76% accurate. External validation showed 74% (Hamburg), 78% (Milan) and 68% (Seattle) accuracy.
CONCLUSIONS: Relative to the previous nomograms (10 predictors or 71% accuracy) our tool relies on fewer variables (6) and shows superior accuracy in European men. Accuracy in American men is substantially lower. Racial, clinical and biochemical differences may explain the observed discrepancy in predictive accuracy.

Entities:  

Mesh:

Year:  2007        PMID: 17222622     DOI: 10.1016/j.juro.2006.09.025

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  17 in total

1.  Risk factors for prostate cancer detection after a negative biopsy: a novel multivariable longitudinal approach.

Authors:  Peter H Gann; Angela Fought; Ryan Deaton; William J Catalona; Edward Vonesh
Journal:  J Clin Oncol       Date:  2010-02-22       Impact factor: 44.544

Review 2.  Role of nomograms for prostate cancer in 2007.

Authors:  Felix K-H Chun; Pierre I Karakiewicz; Hartwig Huland; Markus Graefen
Journal:  World J Urol       Date:  2007-02-27       Impact factor: 4.226

3.  Using biopsy to detect prostate cancer.

Authors:  Shahrokh F Shariat; Claus G Roehrborn
Journal:  Rev Urol       Date:  2008

4.  Prediction of prostate cancer by deep learning with multilayer artificial neural network.

Authors:  Takumi Takeuchi; Mami Hattori-Kato; Yumiko Okuno; Satoshi Iwai; Koji Mikami
Journal:  Can Urol Assoc J       Date:  2018-10-15       Impact factor: 1.862

Review 5.  Critical review of prostate cancer predictive tools.

Authors:  Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino
Journal:  Future Oncol       Date:  2009-12       Impact factor: 3.404

6.  Assessment of long-term outcomes associated with urinary prostate cancer antigen 3 and TMPRSS2:ERG gene fusion at repeat biopsy.

Authors:  Selin Merdan; Scott A Tomlins; Christine L Barnett; Todd M Morgan; James E Montie; John T Wei; Brian T Denton
Journal:  Cancer       Date:  2015-08-17       Impact factor: 6.860

7.  Focal treatment or observation of prostate cancer: pretreatment accuracy of transrectal ultrasound biopsy and T2-weighted MRI.

Authors:  Lucas Nogueira; Liang Wang; Samson W Fine; Rodrigo Pinochet; Jordan M Kurta; Darren Katz; Caroline J Savage; Angel M Cronin; Hedvig Hricak; Peter T Scardino; Oguz Akin; Jonathan A Coleman
Journal:  Urology       Date:  2009-07-30       Impact factor: 2.649

8.  Prostate cancer rates in patients with initially negative elastography-targeted biopsy vs. systematic biopsy.

Authors:  Jeannette Kratzenberg; Georg Salomon; Pierre Tennstedt; Paolo Dell'Oglio; Derya Tilki; Axel Haferkamp; Markus Graefen; Katharina Boehm
Journal:  World J Urol       Date:  2018-01-13       Impact factor: 4.226

Review 9.  The role of biomarkers in the assessment of prostate cancer risk prior to prostate biopsy: which markers matter and how should they be used?

Authors:  Marianne Schmid; Quoc-Dien Trinh; Markus Graefen; Margit Fisch; Felix K Chun; Jens Hansen
Journal:  World J Urol       Date:  2014-05-14       Impact factor: 4.226

10.  Predicting the outcome of prostate biopsy: comparison of a novel logistic regression-based model, the prostate cancer risk calculator, and prostate-specific antigen level alone.

Authors:  David J Hernandez; Misop Han; Elizabeth B Humphreys; Leslie A Mangold; Samir S Taneja; Stacy J Childs; Georg Bartsch; Alan W Partin
Journal:  BJU Int       Date:  2008-10-24       Impact factor: 5.588

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.