Infraclinic breast carcinoma: Application of neural networks techniques for the indication of radioguided biopsias
The aim of this work was to determine if the utilization of an artificial neural network (ANN) for the indication of radioguided biopsias can reduce the percentage of negative biopsias. The ANN was constructed as a three-layer, feed-forward network. The input layer consist of 15 input nodes corresponding to the radiologic and clinico-epidemiological data to evaluate. The ANN was trained using a supervised learning algorithm on 190 cases (122 benign, 68 malignant cases) and tested on 47 cases (30 benign, 17 malignant cases). Performance of the network was evaluated in terms of sensitivity and specificity over a range of decision threshold and was expressed as a receiver operating characteristic curve (ROC).
The ANN performed more accurately than the radiologists with a sensitivity of 1.0 and specificity of 0.6.
An ANN can be trained to predict malignancy from mammographic findings and clinicoepidemiological data with a high degree of accuracy.
Key wordsbreast cancer neural network mammography radioguided biopsia
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