ANALYSIS OF IMAGE CYTOMETRY DATA OF FINE-NEEDLE ASPIRATED CELLS OF BREAST-CANCER PATIENTS - A COMPARISON BETWEEN LOGISTIC-REGRESSION AND ARTIFICIAL NEURAL NETWORKS

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 axillary lymph nodes and histologica l grade using logistic regression. Prediction was 70% for both nodal s tatus 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 imp act of individual prognostic factors. The statistical approach identif ied S-phase fraction and DNA-ploidy as the most important prediction m arkers for nodal status and histological assessment analyses. A compar ison was made between these two quantitative techniques.