In:
ISRN Urology, Hindawi Limited, Vol. 2012 ( 2012-07-05), p. 1-6
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
2090-5815
Language:
English
Publisher:
Hindawi Limited
Publication Date:
2012
detail.hit.zdb_id:
2613000-2
