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Predictive Quantitative Sonographic Features on Classification of Hot and Cold Thyroid Nodules Publisher Pubmed



Ardakani AA1 ; Mohammadzadeh A2 ; Yaghoubi N3 ; Ghaemmaghami Z4 ; Reiazi R1, 5 ; Jafari AH6 ; Hekmat S7 ; Shiran MB1 ; Bitarafanrajabi A1, 3
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
  2. 2. Department of Radiology, Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
  3. 3. Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
  4. 4. Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
  5. 5. Medical Image and Signal Processing Research Core, Iran University of Medical Sciences, Tehran, Iran
  6. 6. Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  7. 7. Department of Nuclear Medicine, School of Medicine, Hasheminejad Hospital, Iran University of Medical Sciences, Tehran, Iran

Source: European Journal of Radiology Published:2018


Abstract

Purpose: This study investigated the potentiality of ultrasound imaging to classify hot and cold thyroid nodules on the basis of textural and morphological analysis. Methods: 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. Results: 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. Conclusions: 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. © 2018 Elsevier B.V.