A model to discriminate malignant from benign thyroid nodules using artificial neural network.

OBJECTIVE: This study aimed to construct a model for using in differentiating benign and malignant nodules with the artificial neural network and to increase the objective diagnostic accuracy of US. MATERIALS AND METHODS: 618 consecutive patients (528 women, 161 men) with 689 thyroid nodules (425 malignant and 264 benign nodules) were enrolled in the present study. The presence and absence of each sonographic feature was assessed for each nodule - shape, margin, echogenicity, internal composition, presence of calcifications, peripheral halo and vascularity on color Doppler. The variables meet the following criteria: important sonographic features and statistically significant difference were selected as the input layer to build the ANN for predicting the malignancy of nodules. RESULTS: Six sonographic features including shape (Taller than wide, p