Browse by author
Lookup NU author(s): Stuart Grey,
Emeritus Professor Satnam Dlay,
Dr Gajanan Sherbet
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Artificial neural network assessment of breast cancer markers can reliably tell us the role and efficacy of genes, steroid receptors and conventional tumour markers as predictors of breast cancer metastasis. 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 of just 41 samples 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 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. (12 References).
Author(s): Grey SR, Dlay SS, Leone BE, Cajone F, Sherbet GV
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Communication Systems, Networks and Digital Signal Processing (CSNDSP)
Year of Conference: 2004
Notes: Dlay SS
Newcastle upon Tyne, UK.
Communication Systems, Networks and Digital Signal Processing. CSNDSP 2004. Fourth International Symposium. Newcastle upon Tyne, UK. 20-22 July 2004.