Image Cytometry Data From Breast Lesions Analyzed using Hybrid Networks.

The treatment and therapy to be administered on breast cancer patients are dependent on the stage of the disease at time of diagnosis. It is therefore crucial to determine the stage at the earliest time possible. Tumor dissemination to axillary lymph nodes has been regarded as an indication of tumor aggression, thus the stage of the disease. Neural networks have been employed in many applications including breast cancer prognosis. The performance of the networks have often been quoted based on accuracy and mean squared error. In this paper, the performance of hybrid networks based on Multilayer Perceptron and Radial Basis Function networks to predict axillary lymph node involvement have been investigated. A measurement of how confident the networks are with respect to the results produced is also proposed. The input layer of the networks include four image cytometry features extracted from fine needle aspiration of breast lesions. The highest accuracy achieved by the hybrid networks was 69% only. However, most of the correctly predicted cases had a high confidence level.

DNA ploidy and cell cycle distribution of breast cancer aspirate cells measured by image cytometry and analyzed by artificial neural networks for their prognostic significance.

Chromosomal abnormalities are commonly associated with cancer, and their importance in the pathogenesis of the disease has been well recognized. Also recognized in recent years is the possibility that, together with chromosomal abnormalities, DNA ploidy of breast cancer aspirate cells, measured by image cytometric techniques, may correlate with prognosis of the disease. Here, we have examined the use of an artificial neural network to predict: 1) subclinical metastatic disease in the regional lymph nodes and 2) histological assessment, through the analysis of data obtained by image cytometric techniques of fine needle aspirates of breast tumors. The cellular features considered were: 1) DNA ploidy measured in terms of nuclear DNA content as well as by cell cycle distribution; 2) size of the S-phase fraction; and 3) nuclear pleomorphism. A further objective of the study was to analyze individual markers in terms of impact significance on predicting outcome in both cases. DNA ploidy, indicated by cell cycle distribution, was found markedly to influence the prediction of nodal spread of breast cancer, and nuclear pleomorphism to a lesser degree. Furthermore, a comparison between histological assessment and artificial neural network prediction shows a closer correlation between the neural approach and the development of further metastases as indicated in subsequent follow-up, than does histological assessment. These data demonstrate that artificial neural networks are capable of providing powerful and reliable indicators of possible lymph node metastasis, using measurements of cellular features alone.

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 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 markers for nodal status and histological assessment analyses. A comparison was made between these two quantitative techniques.