Literature DB >> 17162015

Prediction of indolent prostate cancer: validation and updating of a prognostic nomogram.

E W Steyerberg1, M J Roobol, M W Kattan, T H van der Kwast, H J de Koning, F H Schröder.   

Abstract

PURPOSE: Screening with serum prostate specific antigen testing leads to the detection of many prostate cancers early in their natural history. Statistical models have been proposed to predict indolent cancer. We validated and updated model predictions for a screening setting.
MATERIALS AND METHODS: We selected 247 patients with clinical stage T1C or T2A from the European Randomized Study on Screening for Prostate Cancer who were treated with radical prostatectomy. We validated a nomogram that had previously been developed in a clinical setting. Predictive characteristics were serum prostate specific antigen, ultrasound prostate volume, clinical stage, prostate biopsy Gleason grade, and total length of cancer and noncancer tissue in biopsy cores. Indolent cancer was defined as pathologically organ confined cancer 0.5 cc or less in volume without poorly differentiated elements. Logistic regression was used to update the previous model and examine the contribution of other potential predictors.
RESULTS: Overall 121 of 247 patients (49%) had indolent cancer, while the average predicted probability was around 20% (p <0.001). Effects of individual variables were similar to those found before and discriminative ability was adequate (AUC 0.76). An updated model was constructed, which merely recalibrated the nomogram and did not apply additional predictors.
CONCLUSIONS: Prostate cancers identified in a screening setting have a substantially higher likelihood of being indolent than those predicted by a previously proposed nomogram. However, an updated model can support patients and clinicians when the various treatment options for prostate cancer are considered.

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Year:  2007        PMID: 17162015     DOI: 10.1016/j.juro.2006.08.068

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


  76 in total

1.  Postoperative Nomogram for Predicting Cancer-Specific Mortality in Medullary Thyroid Cancer.

Authors:  Allen S Ho; Lu Wang; Frank L Palmer; Changhong Yu; Arnbjorn Toset; Snehal Patel; Michael W Kattan; R Michael Tuttle; Ian Ganly
Journal:  Ann Surg Oncol       Date:  2014-11-04       Impact factor: 5.344

2.  Selective detection of histologically aggressive prostate cancer: an Early Detection Research Network Prediction model to reduce unnecessary prostate biopsies with validation in the Prostate Cancer Prevention Trial.

Authors:  Stephen B Williams; Simpa Salami; Meredith M Regan; Donna P Ankerst; John T Wei; Mark A Rubin; Ian M Thompson; Martin G Sanda
Journal:  Cancer       Date:  2011-10-17       Impact factor: 6.860

Review 3.  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

4.  Using biopsy to detect prostate cancer.

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

5.  A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI.

Authors:  Nandinee Fariah Haq; Piotr Kozlowski; Edward C Jones; Silvia D Chang; S Larry Goldenberg; Mehdi Moradi
Journal:  Comput Med Imaging Graph       Date:  2014-07-05       Impact factor: 4.790

Review 6.  Prostate cancer nomograms: a review of their use in cancer detection and treatment.

Authors:  R J Caras; Joseph R Sterbis
Journal:  Curr Urol Rep       Date:  2014-03       Impact factor: 3.092

7.  Use of nomograms for predictions of outcome in patients with advanced bladder cancer.

Authors:  Shahrokh F Shariat; Pierre I Karakiewicz; Guilherme Godoy; Seth P Lerner
Journal:  Ther Adv Urol       Date:  2009-04

Review 8.  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

9.  Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine.

Authors:  Hak Jong Lee; Sung Il Hwang; Seok-Min Han; Seong Ho Park; Seung Hyup Kim; Jeong Yeon Cho; Chang Gyu Seong; Gheeyoung Choe
Journal:  Eur Radiol       Date:  2009-12-17       Impact factor: 5.315

10.  [Status of care for prostate cancer in 2008].

Authors:  B Arndt; M Kwiatkowski; F Recker
Journal:  Urologe A       Date:  2008-08       Impact factor: 0.639

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