DocumentCode :
776668
Title :
Neural network-based assessment of prognostic markers and outcome prediction in bilharziasis-associated bladder cancer
Author :
Ji, Wei ; Naguib, Raouf N G ; Ghoneim, Mohamed A.
Author_Institution :
Sch. of Math. & Inf. Sci., Coventry Univ., UK
Volume :
7
Issue :
3
fYear :
2003
Firstpage :
218
Lastpage :
224
Abstract :
In this paper the potential value of two prognostic factors, namely, bilharziasis status and tumor histological type, is investigated in relation to their abilities to predict disease progression and outcome of patients with bladder cancer, using radial basis function (RBF) neural networks. The bladder cancer data set is described by eight clinical and pathological markers. Two outcomes are of interest: either a patient is alive and free of disease or the patient is dead within five years of diagnosis. Three hundred and twenty-one (321) patients are involved in this retrospective study, 83.5% of whom had been confirmed with bilharziasis history. Selected marker subsets are examined to improve the outcome predictive accuracy and to evaluate the effects of the assessed prognostic factors on such outcome. The highest predictive accuracy for patients with bladder adenocarcinoma, as obtained from the RBF network, is found to be 85% with one subset of markers. The predictive analysis shows that bilharziasis history and patients´ histology type are both important prognostic factors in prediction and, for each histology type, different marker combinations with significant characteristics have been observed.
Keywords :
cancer; medical computing; radial basis function networks; tumours; RBF network; bilharziasis-associated bladder cancer; bladder adenocarcinoma; disease; disease progression; neural network; outcome prediction; prognostic markers; radial basis function neural networks; survival analysis; tumor histological type; Accuracy; Bladder; Cancer; Diseases; History; Intelligent networks; Neoplasms; Neural networks; Pathology; Radial basis function networks; Adult; Aged; Algorithms; Disease-Free Survival; Female; Humans; Male; Middle Aged; Neural Networks (Computer); Prognosis; Risk Assessment; Risk Factors; Schistosomiasis; Survival Analysis; Survival Rate; Urinary Bladder Neoplasms;
fLanguage :
English
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-7771
Type :
jour
DOI :
10.1109/TITB.2003.813796
Filename :
1229861
Link To Document :
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