title: Cancer evolution assessment using artificial neural networks.
abstract: Artificial Neural Networks have demonstrated good utility to establish disease prognostic, if there are correctly trained. Our aim was to realize Artificial Neural Networks in different cancer types, to evaluate time survival. For the beginning this was applied for breast cancer. The conclusion is that the homogeneity of data entrance sets, the number, and their coding in relation with their importance for cancer prognostic are decisive for the results of the trained ANN. These results can be useful in the physician clinical decision.
authors.affiliation: Gr.T. Popa University of Medicine and Pharmacy Iaşi, School of Medicine, Biophysics and Nuclear Medicine Department.
authors.forename: Cipriana
authors.initials: C
authors.lastname: Stefanescu
authors.forename: L V
authors.initials: LV
authors.lastname: Boiculese
authors.forename: V
authors.initials: V
authors.lastname: Rusu
authors.forename: Mihaela
authors.initials: M
authors.lastname: Costin
authors.forename: D
authors.initials: D
authors.lastname: Hountis
authors.forename: Elena
authors.initials: E
authors.lastname: Muşat
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms; D005260:Female; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D016019:Survival Analysis