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Artificial neural network-based analysis of molecular markers for predicting metastatic spread of breast cancer.

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 turnout in the regional lymph nodes. The relative expression of S100A4 and nm23 genes is the single most effective predictor of nodal status. 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: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 7Th World Multiconference on Systemics, Cybernetics and Informatics,

Year of Conference: 2003

Pages: 17-20

Publisher: International Institute of Informatics and Cybernetics

Library holdings: Search Newcastle University Library for this item

ISBN: 9806560019