@inproceedings{98497af7364a4a06b3dc70cab8630955,
title = "Comparison between artificial neural networks and urologists' assessment of outcome in bladder cancer - Part II: Survival in muscle-invasive (T2-T4) tumours",
abstract = "Currently, we lack accurate methods of predicting survival in patients with muscle-invasive bladder cancer. Data relating to 40 such patients (out of a comprehensive database of 212 patients) was retrospectively analyzed by artificial neural networks (ANNs). A total of 15 different factors including clinicopathological and molecular markers of mixed prognostic significance were used in the analysis. The accuracy of the ANN in predicting 12-months cancer-specific survival for T2-T4 cancers was 82%. This was subsequently compared with the predictions of four experienced urologists who analyzed the same data blindly. The corresponding mean accuracy for the urologists was 65%.",
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 = "1234",
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",
}