Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

The early accurate determination of course of disease in Ta/T1 bladder cancers is an important issue in patient management and improvement of clinical outcome. For this purpose a comprehensive database of patients with newly diagnosed bladder cancer was retrospectively analyzed by artificial neural networks (ANNs) as follows. First, stage progression in 105 patients with Ta/T1 tumours was analyzed using 7 different factors including clinicopathological and molecular markers of mixed prognostic significance. Eight additional factors were then employed to analyze tumour recurrence within 6 months in 56 patients. The prediction accuracies of the ANNs were subsequently compared to those of 4 expert urologists and proved to be significantly higher in predicting stage progression. An important result of the analysis concerned the T1G3 group of tumours which is non-infiltrative at diagnosis, but has the greatest propensity to progress to muscle-invasive disease. In this group, again, the performance of the ANN exceeded that of the urologists.

Type

Conference paper

Publication Date

01/12/1999

Volume

2