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

Lookup NU author(s): Stuart Grey, Emeritus Professor Satnam Dlay, Dr Gajanan Sherbet


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The expression of tumour promoter gene S100A4, metastasis suppressor gene nm23, oestrogen and progesterone receptors, and tumour grade and size have been investigated for their potential to predict breast cancer progression. The molecular and cellular data have been analysed using artificial neural networks to determine the potential of these markers to predict the presence of metastatic tumour in the regional lymph nodes. This study shows that tumour grade and size are poor predictors. The relative expression of S100A4 and nm23 genes is the single most effective predictor of nodal status. Inclusion of oestrogen- and progesterone-receptor status with tumour grade and size markers improves prediction; however, there may be some overlap between steroid receptors and molecular markers. This study also underscores the power of artificial neural network techniques to predict the potential of primary breast cancers to spread to axillary lymph nodes. This could aid the clinician in determining whether invasive procedures of axially node dissection can be obviated and whether conservative forms of treatment might be appropriate in the management of the patient.

Publication metadata

Author(s): Grey SR, Dlay SS, Leone BE, Cajone F, Sherbet GV

Publication type: Article

Publication status: Published

Journal: Clinical and Experimental Metastasis

Year: 2003

Volume: 20

Issue: 6

Pages: 507-514

ISSN (print): 0262-0898

ISSN (electronic): 1573-7276

Publisher: Springer


DOI: 10.1023/A:1025846019656

PubMed id: 14598884


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