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Quantitative Measurement for Thyroid Cancer Characterization Based on Elastography
Author(s) -
Ding Jianrui,
Cheng Hengda,
Ning Chunping,
Huang Jianhua,
Zhang Yingtao
Publication year - 2011
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.7863/jum.2011.30.9.1259
Subject(s) - medicine , hue , elastography , thyroid nodules , receiver operating characteristic , thyroid cancer , thyroid , artificial intelligence , radiology , color space , pattern recognition (psychology) , ultrasound , computer science , image (mathematics)
Objectives The purpose of this study was to evaluate color thyroid elastograms quantitatively and objectively and select more effective features to differentiate benign from malignant thyroid nodules. Methods The study was approved by the Ethics Committee of Harbin Medical University. A total of 125 cases (56 malignant and 69 benign) were analyzed in this retrospective study. The original color thyroid elastograms were transferred from the red‐green‐blue color space to the hue‐saturation‐value color space. The elasticity information was represented by the hue component of color elastograms. The lesion regions were delineated by radiologists, and statistical and textural features were extracted. Then the most effective and reliable features among them were selected by using a minimum redundancy–maximum relevance algorithm. The selected features were input to a support vector machine to differentiate benign from malignant thyroid nodules. Results The classification accuracy was 93.6% when the hard area ratio and textural feature (energy) of the lesion region were used. The area under the receiver operating characteristic curve for the hard area ratio was higher than that for the strain ratio (0.97 versus 0.87; P < .01), and the area under the curve for the hard area ratio was also higher than that for the color score (0.97 versus 0.80; P < .001). The results also showed that the features were robust for lesion region delineation. Conclusions The hard area ratio is an important and quantitative metric for elastograms. Quantitative analysis of elastograms using computer‐aided diagnostic techniques can improve diagnostic accuracy.