ABSTRACT
This chapter demonstrates that artificial neural networks (ANNs) are capable of predicting lymph node metastasis in breast cancer patients using measurements relating to the expression of specific markers. It shows that cellular features such as DNA ploidy, size of the S-phase fraction, cell cycle distribution, and nuclear pleomorphism of breast cancer fine-needle aspirates cells could be analysed using ANNs and successfully used to predict subclinical metastatic disease. Genetic instability often manifests itself in neoplasms in the form of chromosomal and DNA aneuploidy, altered DNA repair properties, gene amplification, and deletion and point mutations. DNA aneuploidy has been correlated with early recurrence of endometrial carcinomas, as well as with the degree of myometrial invasion by the tumour. The detection of tumour dissemination to the regional lymph nodes is of paramount importance in the management of the disease. DNA aneuploidy might be a consequence of cells entering the S-phase prematurely.
