Predictive quantitative sonographic features on classification of hot and cold thyroid nodules

This study investigated the potentiality of ultrasound imaging to classify hot and cold thyroid nodules on the basis of textural and morphological analysis.

In this research, 42 hypo (hot) and 42 hyper-function (cold) thyroid nodules were evaluated through the proposed method of computer aided diagnosis (CAD) system. To discover the difference between hot and cold nodules, 49 sonographic features (9 morphological, 40 textural) were extracted. A support vector machine classifier was utilized for the classification of LNs based on their extracted features.

In the training set data, a combination of morphological and textural features represented the best performance with area under the receiver operating characteristic curve (AUC) of 0.992. Upon testing the data set, the proposed model could classify the hot and cold thyroid nodules with an AUC of 0.948.

CAD method based on textural and morphological features is capable of distinguishing between hot from cold nodules via 2-Dimensional sonography. Therefore, it can be used as a supplementary technique in daily clinical practices to improve the radiologists' understanding of conventional ultrasound imaging for nodules characterization.