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Analysis of image cytometry data of fine needle aspirated cells of breast cancer patients: A comparison between logistic regression and artificial neural networks

Lookup NU author(s): Dr Madurai Lakshmi, Dr Viney Wadehra, Professor Thomas Lennard, Dr Gajanan Sherbet


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Image flow cytometry data of aspirated tumour cells from 102 patients with breast cancer were analysed and used as prognostic markers in an attempt to predict involvement of axilary lymph nodes and histological grade using logistic regression. Prediction was 70% for both nodal status and histological analyses. The outcome of this study is compared to an earlier study using the same cytological information to obtain prediction using a neural approach. Using artificial neural networks, prediction accuracy was 87% and 82% for nodal status and histological assessment, respectively. This study also attempts to identify the impact of individual prognostic factors. The statistical approach identified S-phase fraction and DNA-ploidy as the most important prediction markets for nodal status and histological assessment analyses. A comparison was made between these two quantitative techniques.

Publication metadata

Author(s): Mat-Sakim, H., Naguib, R., Lakshmi, M., Wadehra, V., Lennard, T.W.J., Bhatavdekar, J., Sherbet, G.V.

Publication type: Article

Publication status: Published

Journal: Anticancer Research

Year: 1998

Volume: 18

Issue: 4 A

Pages: 2723-2726

Print publication date: 01/07/1998

ISSN (print): 0250-7005

ISSN (electronic):

PubMed id: 9703935