Neural network analysis of clinicopathological and molecular markers in bladder cancer.
OBJECTIVE
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.


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.
Qureshi
K N
KN
Department of Urology, Royal Hallamshire Hospital, Sheffield, United Kingdom.
Naguib
R N
RN
Hamdy
F C
FC
Neal
D E
DE
Mellon
J K
JK
D014408:Biomarkers, Tumor; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000401:mortality*; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D015996:Survival Rate; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis* / Q000401:mortality*
D014408:Biomarkers, Tumor