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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Naguib, R.N.G. Qureshi, K.N. Hamdy, F.C. Neal, D.E. Mellon, J.K. |
| Copyright Year | 1999 |
| Description | Author affiliation: BIOCORE, Coventry Univ., UK (Naguib, R.N.G.) |
| 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 analysed 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 analysed the same data blindly. The corresponding mean accuracy for the urologists was 65%. |
| File Size | 99000 |
| File Format | |
| ISBN | 0780356748 |
| ISSN | 1094687X |
| DOI | 10.1109/IEMBS.1999.804400 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-10-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Artificial neural networks Intelligent networks Bladder Cancer Tumors Diseases Hospitals Databases Metastasis Oncological surgery |
| Content Type | Text |
| Resource Type | Article |
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