Application of artificial neural networks for diagnosis of breast cancer

Abstract

We review four current projects pertaining to artificial neural network (ANN) models that merge radiologist-extracted findings to perform computer aided diagnosis (CADx) of breast cancer. These projects are: (1) prediction of breast lesion malignancy using mammographic findings; (2) classification of malignant lesions as in situ vs. invasive cancer; (3) prediction of breast mass malignancy using ultrasound findings; and (4) the evaluation of CADx models in a cross-institution study. These projects share in common the use of feedforward error backpropagation ANNs. Inputs to the ANNs are medical findings such as mammographic or ultrasound lesion descriptors and patient history data. The output is the biopsy outcome (benign vs. malignant, or in situ vs. invasive cancer) which is being predicted. All ANNs undergo supervised training using actual patient data. These ANN decision models may assist in the management of patients with breast lesions, such as by reducing the number of unnecessary surgical procedures and their associated cost.