@inproceedings{a2ffdffd34274719b5daf3fafa3ef772,
title = "Comparison between artificial neural networks and urologists' assessment of outcome in bladder cancer - Part I: Progression and recurrence in Ta/T1 tumours",
abstract = "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.",
author = "Naguib, {R. N.G.} and Qureshi, {K. N.} and Hamdy, {F. C.} and Neal, {D. E.} and Mellon, {J. K.}",
year = "1999",
language = "Ingl{\'e}s",
isbn = "0780356756",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
publisher = "IEEE",
pages = "1233",
booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
note = "Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) ; Conference date: 13-10-1999 Through 16-10-1999",
}