Prediction of nodal spread of breast cancer by using artificial neural network-based analyses of S100A4, nm23 and steroid receptor expression.

Grey, S R; Dlay, S S; Leone, B E; Cajone, F; Sherbet, G V

Grey, S R; Dlay, S S; Leone, B E; Cajone, F; Sherbet, G V.

Affiliation

Grey SR; School of Electrical, Electronic and Computer Engineering, University of Newcastle upon Tyne, UK.

Clin Exp Metastasis ; 20(6): 507-14, 2003.

Article in En | MEDLINE | ID: mdl-14598884

Subject(s)

Breast Neoplasms/pathology; Lymphatic Metastasis/pathology; Monomeric GTP-Binding Proteins/genetics; Nerve Net; Nucleoside-Diphosphate Kinase; Receptors, Steroid/genetics; S100 Proteins/genetics; Transcription Factors/genetics; Adult; Aged; Aged, 80 and over; Female; Gene Expression Regulation, Neoplastic; Genes, Tumor Suppressor; Genetic Markers; Humans; Middle Aged; Models, Theoretical; NM23 Nucleoside Diphosphate Kinases; Predictive Value of Tests; Promoter Regions, Genetic; Receptors, Estrogen/genetics; Receptors, Progesterone/genetics; S100 Calcium-Binding Protein A4

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Collection: 01-internacional Database: MEDLINE Main subject: Transcription Factors / Breast Neoplasms / S100 Proteins / Receptors, Steroid / Nucleoside-Diphosphate Kinase / Monomeric GTP-Binding Proteins / Lymphatic Metastasis / Nerve Net Type of study: Prognostic_studies / Risk_factors_studies Limits: Adult / Aged / Aged80 / Female / Humans / Middle aged Language: En Journal: Clin Exp Metastasis Journal subject: NEOPLASIAS Year: 2003 Document type: Article Affiliation country: United kingdom Country of publication: Netherlands

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