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Lookup NU author(s): Stuart Grey,
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.
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.
Author(s): Grey SR, Dlay SS, Leone BE, Cajone F, Sherbet GV
Publication type: Article
Publication status: Published
Journal: Clinical and Experimental Metastasis
ISSN (print): 0262-0898
ISSN (electronic): 1573-7276
PubMed id: 14598884
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