Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

Abstract

Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

Value of Artificial Neural Network Ultrasound in Improving Breast Cancer Diagnosis

“…The number of specimens obtained is small, and the source of the mass tissue cannot be further characterized. Therefore, it is believed that the results of thick-needle aspiration biopsy are more useful in the diagnosis of breast lesions than fine-needle aspiration cytology [ 1 ]. Huynh et al compared the differences in the ultrasound appearance of the two groups of lesions, and the analysis suggested that there is an underestimated ultrasound appearance of precancerous lesions [ 2 ].…”