Title :
A comparison between artificial neural networks and urologists´ assessment of outcome in bladder cancer. II. Survival in muscle-invasive (T2-T4) tumours
Author :
Naguib, R.N.G. ; Qureshi, K.N. ; Hamdy, F.C. ; Neal, D.E. ; Mellon, J.K.
Author_Institution :
BIOCORE, Coventry Univ., UK
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%
Keywords :
biological organs; cancer; patient diagnosis; radial basis function networks; self-organising feature maps; tumours; ANN assessment; RBF algorithm; T2-T4 cancers; bladder cancer outcome; cancer-specific survival; clinicopathological markers; mean accuracy; mixed prognostic significance; molecular markers; muscle-invasive tumours; self-organising maps; survival prediction; urologist assessment; Artificial neural networks; Bladder; Cancer; Databases; Diseases; Hospitals; Intelligent networks; Metastasis; Oncological surgery; Tumors;
Conference_Titel :
[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-5674-8
DOI :
10.1109/IEMBS.1999.804400
