CONTEXT: The sensitivity and specificity profile of measuring levels of prostate-specific antigen (PSA) to select men for prostate biopsy is not optimal. This has prompted the construction of nomograms and artificial neural networks (ANNs) to increase the performance of PSA measurements. OBJECTIVE: A systematic review of nomograms and ANNs designed to predict the risk of a positive prostate biopsy for cancer was conducted in order to determine their value versus measuring PSA levels alone. EVIDENCE ACQUISITION: Medical Literature Analysis and Retrieval System Online (U.S. National Library of Medicine's life science database; MEDLINE) was searched using the terms "nomogram" "artificial neural network" and "prostate cancer" for dates up to and including July 2007 and was supplemented by manual searches of reference lists. Included studies used an assessment tool to examine the risk of a positive prostate biopsy in a man without a known cancer diagnosis. Intramodel comparisons with evaluation of PSA levels alone, and intermodel comparisons of area under the curve (AUC) from receiver operating characteristic (ROC) curves were conducted. Individual case examples were also used for comparisons. EVIDENCE SYNTHESIS: Twenty-three studies examining 36 models were included. With the exception of two studies, all the models had AUC values of 0.70 or greater, with eight reporting an AUC of >/=0.80 and four (all ANNs) reporting an AUC >/=0.85, with variable validation status. Fourteen studies compared the AUC with PSA levels alone: all showed a benefit from using AUCs which varied from 0.02 to 0.26. Of the 16 external validation comparisons, in 13 the AUC was lower in the external population than in the model population. CONCLUSIONS: Nomograms and ANNs produce improvements in AUC over measurement of PSA levels alone, but many lack external validation. Where this is available, the benefits are often diminished, although most remain significantly better than with evaluation of PSA levels alone. In men without additional risk factors, PSA cutoff values alone provide a relatively precise risk estimate, but if additional risk factors are known, PSA values alone are less accurate.
CONTEXT: The sensitivity and specificity profile of measuring levels of prostate-specific antigen (PSA) to select men for prostate biopsy is not optimal. This has prompted the construction of nomograms and artificial neural networks (ANNs) to increase the performance of PSA measurements. OBJECTIVE: A systematic review of nomograms and ANNs designed to predict the risk of a positive prostate biopsy for cancer was conducted in order to determine their value versus measuring PSA levels alone. EVIDENCE ACQUISITION: Medical Literature Analysis and Retrieval System Online (U.S. National Library of Medicine's life science database; MEDLINE) was searched using the terms "nomogram" "artificial neural network" and "prostate cancer" for dates up to and including July 2007 and was supplemented by manual searches of reference lists. Included studies used an assessment tool to examine the risk of a positive prostate biopsy in a man without a known cancer diagnosis. Intramodel comparisons with evaluation of PSA levels alone, and intermodel comparisons of area under the curve (AUC) from receiver operating characteristic (ROC) curves were conducted. Individual case examples were also used for comparisons. EVIDENCE SYNTHESIS: Twenty-three studies examining 36 models were included. With the exception of two studies, all the models had AUC values of 0.70 or greater, with eight reporting an AUC of >/=0.80 and four (all ANNs) reporting an AUC >/=0.85, with variable validation status. Fourteen studies compared the AUC with PSA levels alone: all showed a benefit from using AUCs which varied from 0.02 to 0.26. Of the 16 external validation comparisons, in 13 the AUC was lower in the external population than in the model population. CONCLUSIONS: Nomograms and ANNs produce improvements in AUC over measurement of PSA levels alone, but many lack external validation. Where this is available, the benefits are often diminished, although most remain significantly better than with evaluation of PSA levels alone. In men without additional risk factors, PSA cutoff values alone provide a relatively precise risk estimate, but if additional risk factors are known, PSA values alone are less accurate.
Authors: Andrew J Vickers; Angel M Cronin; Gunnar Aus; Carl-Gustav Pihl; Charlotte Becker; Kim Pettersson; Peter T Scardino; Jonas Hugosson; Hans Lilja Journal: Cancer Date: 2010-06-01 Impact factor: 6.860
Authors: Matthew J Watson; Arvin K George; Mahir Maruf; Thomas P Frye; Akhil Muthigi; Michael Kongnyuy; Subin G Valayil; Peter A Pinto Journal: Future Oncol Date: 2016-07-12 Impact factor: 3.404
Authors: Andrew J Vickers; Angel M Cronin; Monique J Roobol; Jonas Hugosson; J Stephen Jones; Michael W Kattan; Eric Klein; Freddie Hamdy; David Neal; Jenny Donovan; Dipen J Parekh; Donna Ankerst; George Bartsch; Helmut Klocker; Wolfgang Horninger; Amine Benchikh; Gilles Salama; Arnauld Villers; Steve J Freedland; Daniel M Moreira; Fritz H Schröder; Hans Lilja Journal: Clin Cancer Res Date: 2010-08-24 Impact factor: 12.531
Authors: Andrew J Vickers; Angel M Cronin; Thomas Björk; Jonas Manjer; Peter M Nilsson; Anders Dahlin; Anders Bjartell; Peter T Scardino; David Ulmert; Hans Lilja Journal: BMJ Date: 2010-09-14