Computer-aided diagnosis for the differentiation of malignant from benign thyroid nodules on ultrasonography

We sought to evaluate the diagnostic performance of an artificial neural network (ANN) and binary logistic regression (BLR) in differentiating malignant from benign thyroid nodules on ultrasonography.

Two experienced radiologists, who were unaware of the histopathological diagnosis, analyzed ultrasonographic (US) features of 109 pathologically proven thyroid lesions (49 malignant and 60 benign) in 96 patients. Each radiologist was asked to evaluate US findings and categorize nodules into one of the two groups (malignant vs. benign) in each case. The following 8 US parameters were assessed for each nodule: size, shape, margin, echogenicity, cystic change, microcalcification, macrocalcification, and halo sign. Statistically significant US findings were obtained with backward stepwise logistic regression and were used for training and testing of the ANN and the BLR. The performance of the ANN and BLR was compared to that of the radiologists using receiver-operating characteristic (ROC) analysis.

Statistically significant US findings were size, margin, echogenicity, cystic change, and macrocalcification of the nodules. The area under the ROC curve (Az) values of ANN and BLR were 0.9492 +/- 0.0195 and 0.9046 +/- 0.0289, respectively. The Az value was 0.8300 +/- 0.0359 for reader 1 and 0.7600 +/- 0.0409 for reader 2. The Az values for ANN and BLR were significantly higher than those for both radiologists (all p < .05).

The performance of the ANN and the BLR was better than that of the radiologists in the distinction of benign and malignant thyroid nodules.