CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold.
Authors of this article are:
Abbasian Ardakani A, Bitarafan-Rajabi A, Mohammadi A, Hekmat S, Tahmasebi A, Shiran MB, Mohammadzadeh A.
A summary of the article is shown below:
OBJECTIVES: The aim of this study was to evaluate if the analysis of sonographic parameters could predict if a thyroid nodule was hot or cold.METHODS: Overall, 102 thyroid nodules, including 51 hyperfunctioning (hot) and 51 hypofunctioning (cold) nodules, were evaluated in this study. Twelve sonographic features (i.e., seven B-mode and five Doppler features) were extracted for each nodule type. The isthmus thickness, nodule volume, echogenicity, margin, internal component, microcalcification, and halo sign features were obtained in the B-mode, while the vascularity pattern, resistive index (RI), peak systolic velocity, end diastolic velocity, and peak systolic/end diastolic velocity ratio (SDR) were determined, based on Doppler ultrasounds. All significant features were incorporated in the computer-aided diagnosis (CAD) system to classify hot and cold nodules.RESULTS: Among all sonographic features, only isthmus thickness, nodule volume, echogenicity, RI, and SDR were significantly different between hot and cold nodules. Based on these features in the training dataset, the CAD system could classify hot and cold nodules with an area under the curve (AUC) of 0.898. Also, in the test dataset, hot and cold nodules were classified with an AUC of 0.833.CONCLUSIONS: 2D sonographic features could differentiate hot and cold thyroid nodules. The CAD system showed a great potential to achieve it automatically.KEY POINTS: • Cold nodules represent higher volume (p = 0.005), isthmus thickness (p = 0.035), RI (p = 0.020), and SDR (p = 0.044) and appear hypoechogenic (p = 0.010) in US. • Nodule volume with an AUC of 0.685 and resistive index with an AUC of 0.628 showed the highest classification potential among all B-mode and Doppler features respectively. • The proposed CAD system could distinguish hot nodules from cold ones with an AUC of 0.833 (sensitivity 90.00%, specificity 70.00%, accuracy 80.00%, PPV 87.50%, and NPV 75.00%).
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This article is a good source of information and a good way to become familiar with topics such as: Machine learning;Radionuclide imaging;Thyroid nodule;Thyrotropin;Ultrasonography, Doppler.