Please use this identifier to cite or link to this item:
Title: Decision tree analysis for prostate cancer prediction
Authors: Stojadinovic, Miroslav
Stojadinovic M.
Pantic D.
Journal: Srpski Arhiv za Celokupno Lekarstvo
Issue Date: 1-Jan-2019
Abstract: © 2019, Serbia Medical Society. All rights reserved. Introduction/Objective The use of serum prostate-specific antigen (PSA) test has dramatically increased the number of men undergoing prostate biopsy. However, the best possible strategies for selecting appropriate patients for prostate biopsy have yet to be defined. The aim of the study was to develop a classification and regression tree (CART) model that could be used to identify patients with significant prostate cancer (PCa) on prostate biopsy in patients referred due to abnormal PSA, digital rectal examination (DRE) findings, or both, regardless of the PSA level. Methods The data on clinicopathological characteristics regarding prebiopsy assessment collected from patients who had undergone ultrasound-guided prostate biopsies included the following: age, PSA, DRE, volume of the prostate, and PSA density (PSAD). The CART analysis was carried out using all predictors identified by univariate logistic regression analysis. Different aspects of predictive performance and clinical utility risk prediction model were assessed. Results In this retrospective study, significant PCa was detected in 92 (41.6%) out of 221 patients. The CART model had three splits based on PSAD, as the most decisive variable, prostate volume, DRE, and PSA. Our model resulted in an 83.3% area under the receiver operating characteristic curve. Decision curve analysis showed that the regression tree provided net benefit for relevant threshold probabilities compared with the logistic regression model, PSAD, and the strategy of biopsying all patients. Conclusion The model helps to reduce unnecessary biopsies without missing significant PCa.
Type: article
DOI: 10.2298/SARH181127039S
ISSN: 03708179
SCOPUS: 85064223179
Appears in Collections:Faculty of Medical Sciences, Kragujevac

Page views(s)




Files in This Item:
File Description SizeFormat 
10.2298-SARH181127039S.pdf426.83 kBAdobe PDFThumbnail

Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.