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Lookup NU author(s): Dr Khaver Qureshi, Dr Hisham Hamdalla, Professor David Neal, Kilian Mellon
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Purpose: To evaluate retrospectively the ability of an artificial neural network (ANN) to predict bladder cancer recurrence within 6 months of diagnosis and stage progression in patients with Ta/T1 bladder cancer, and 12-month cancer-specific survival in patients with T2-T4 bladder cancer. Materials and Methods: Data were analyzed using a NeuralWorks Professional II/Plus software package. The input neural data consisted of clinicopathological and molecular characteristics. Distinct patient groups were used for the prediction of stage progression and tumor recurrence in Ta/T1 bladder cancers, and 12-month cancer-specific survival for patients with T2-T4 tumors. ANN predictions were compared with those of four consultant urologists. Results: The accuracy of the neural network in predicting stage progression and recurrence within 6 months for Ta/T1 tumors and 12-month cancer-specific survival for T2-T4 cancers was 80%, 75% and 82% respectively; with corresponding figures for clinicians being 74%, 79% and 65%. On restricting the validation subset to patients with T1G3 tumors in relation to stage progression, the sensitivity of the ANN analysis increased to 100% with a specificity of 78% and an overall accuracy of 82%. The performance of the ANN in predicting stage progression in T1G3 tumors was significantly higher than that of clinicians (p = 0.25 for the ANN and p = 0.008 for clinicians, McNemar test). Conclusions: Data analysis using an ANN has been shown to be a useful adjunct in predicting outcomes in patients with bladder cancer and out-performs clinicians' predictions of stage progression in the high risk group of patients with T1G3 disease.
Author(s): Hamdy FC; Qureshi KN; Mellon JK; Neal DE; Naguib RNG
Publication type: Article
Publication status: Published
Journal: Journal of Urology
Year: 2000
Volume: 163
Issue: 2
Pages: 630-633
ISSN (print): 0022-5347
ISSN (electronic): 1527-3792
Publisher: Elsevier
URL: http://www.ncbi.nlm.nih.gov/pubmed/10647699
PubMed id: 10647699
