Thyroid nodule classification using ultrasound elastography via linear discriminant analysis

Abstract

The non-surgical diagnosis of thyroid nodules is currently made via a fine needle aspiration (FNA) biopsy. It is estimated that somewhere between 250,000 and 300,000 thyroid FNA biopsies are performed in the United States annually. However, a large percentage (approximately 70%) of these biopsies turn out to be benign. Since the aggressive FNA management of thyroid nodules is costly, quantitative risk assessment and stratification of a nodule’s malignancy is of value in triage and more appropriate healthcare resources utilization. In this paper, we introduce a new method for classifying the thyroid nodules based on the ultrasound (US) elastography features. Unlike approaches to assess the stiffness of a thyroid nodule by visually inspecting the pseudo-color pattern in the strain image, we use a classification algorithm to stratify the nodule by using the power spectrum of strain rate waveform extracted from the US elastography image sequence. Pulsation from the carotid artery was used to compress the thyroid nodules. Ultrasound data previously acquired from 98 thyroid nodules were used in this retrospective study to evaluate our classification algorithm. A classifier was developed based on the linear discriminant analysis (LDA) and used to differentiate the thyroid nodules into two types: (I) no FNA (observation-only) and (II) FNA. Using our method, 62 nodules were classified as type I, all of which were benign, while 36 nodules were classified as Type-II, 16 malignant and 20 benign, resulting in a sensitivity of 100% and specificity of 75.6% in detecting malignant thyroid nodules. This indicates that our triage method based on US elastography has the potential to substantially reduce the number of FNA biopsies (63.3%) by detecting benign nodules and managing them via follow-up observations rather than an FNA biopsy.