Paper Info
Title
Estimation of bladder muscle invasion in transitional cell carcinoma by using artificial neural networks: A study based on prebiopsy imaging findings
Abstract
Purpose: To identify the capability of ANNs in estimation of muscle invasive disease in transitional cell carcinomas (TCC). Materials and methods: In this study, we developed a MLP based ANN to detect muscle invasive disease in transitional cell carcinoma of bladder through the analysis of prebiopsy imaging data. The study includes 172 patients (116 males and 56 females; mean age, 63.92 years; range, 31-92 years) who had had the definitive diagnosis of Transitional cell carcinomas (TCC) based on biopsy results. Results: In the test group, 34 out of 35 cases were correctly classified by he MLP based Neural Network with only one false negative case. The sensitivity, specificity, positive predictive and negative predictive values calculated from the output data were 100%, 96.1%, 90%, and 100%, respectively. Conclusion: The proposed algorithm produced high sensitivity and specifity in predicting the histopathologic results, which shows that this method has a promising value in estimation of bladder muscle invasion in TCC.
Year
DOI
Venue
2008
10.1016/j.eswa.2006.12.011
Expert Syst. Appl.
Keywords
Field
DocType
biopsy result,output data,bladder muscle invasion,transitional cell carcinomas (tcc),transitional cell carcinoma,artificial neural network,prebiopsy imaging finding,negative predictive value,prebiopsy imaging data,muscle invasive disease,high sensitivity,neural network,false negative case,artificial neural networks (ann)
Transitional Cell,Computer science,Biopsy,Artificial intelligence,Radiology,Artificial neural network,Transitional cell carcinoma,Machine learning
Journal
Volume
Issue
ISSN
34
2
Expert Systems With Applications
Citations 
PageRank 
References 
0
0.34
1
Authors
5
Name
Order
Citations
PageRank
Mahmut Tokmakçi173.36
Nuri Erdogan2153.28
Nurettin Sahin300.34
Hulya Akgun430.76
Oguz Ekmekcioglu500.34