Improved artificial neural networks in prediction of malignancy of lesions in contrast‐enhanced MR‐mammography

Abstract

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

A Vector Machine Formulation with Application to the Computer-Aided Diagnosis of Breast Cancer from DCE-MRI Screening Examinations

“…Supervised learning techniques have been applied to breast MRI data in multiple studies [6,[11][12][13]. Previous work in the scientific literature on assessing the vascular heterogeneity of breast cancer from MRI examinations has involved the use of co-occurrence matrix texture analysis [14,15].…”

Classification of Dynamic Contrast-Enhanced Magnetic Resonance Breast Lesions by Support Vector Machines

“…Artificial neural networks have been one of the most common approaches for researching the classification of malignant and benign breast MR lesions [4]- [10]. Jacobs et al have proposed the use of self organizing maps, an unsupervised learning approach using artificial neural networks, to assist in the separation of malignant and benign breast MR lesions [11].…”

Detection and classification of contrast‐enhancing masses by a fully automatic computer‐assisted diagnosis system for breast MRI

“…For integrating all extracted morphologic and dynamic features, ANNs were used (21,23,24). Feed-forward selfreflexive ANNs had been trained in advance of this investigation on the list of the above-described morphologic and dynamic parameters.…”

“…Although recent developments have focused on the automatic, computerassisted analysis of morphologic MRM features, eg, by the use of a classification tool, such as an artificial neural network (ANN), clinical assessment of MRM is still based on the observer's interpretation of morphology (11,13,15,(21)(22)(23)(24). There is clinical interest to integrate observer-independent morphologic analysis into ubiquitously available CAD systems and to link the computer-extracted features to the BI-RADS descriptors.…”