Background:Multivariate models are used to increase prostate cancer (PCa) detection rate and to reduce unnecessary biopsies.
An external validation of the artificial neural network (ANN) "ProstataClass" (ANN-Charité) was performed with daily routine data.
Materials and Methods:The individual ANN predictions were generated with the use of the ANN application for PSA and free PSA assays, which rely on age, tPSA, %fPSA, prostate volume, and DRE (ANN-Charité). Diagnostic validity of tPSA, %fPSA, and the ANN was evaluated by ROC curve analysis and comparisons of observed versus predicted probabilities.
Results:Overall, 101 (35.8%) PCa were detected. The areas under the ROC curve (AUCs) were 0.501 for tPSA, 0.669 for %fPSA, 0.694 for ANN-Charité, 0.713 for nomogram I, and 0.742 for nomogram II, showing a significant advantage for nomogram II (P = 0.009) compared with %fPSA while the other model did not differ from %fPSA (P = 0.15 and P = 0.41). All models overestimated the predicted PCa probability.
Conclusions: Beside ROC analysis, calibration is an important tool to determine the true value of using a model in clinical practice. The worth of multivariate models is limited when external validations were performed without knowledge of the circumstances of the model's development.
Written by:
Ecke TH, Hallmann S, Koch S, Ruttloff J, Cammann H, Gerullis H, Miller K, Stephan C. Are you the author?
Department of Urology, HELIOS Hospital, 15526 Bad Saarow, Germany.
Reference: ISRN Urol. 2012;2012:643181.
doi: 10.5402/2012/643181
PubMed Abstract
PMID: 22830050
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