Added value of mass characteristic frequency to 2-D shear wave elastography for differentiation of benign and malignant thyroid nodules

Kohlenberg, Jacob D.; Gu, Juanjuan; Parvinian, Ahmad; Webb, Jeremy; Kawkgi, Omar M. El; Larson, Nicholas B.; Ryder, Mabel; Fatemi, Mostafa; Alizad, Azra

doi:10.1016/j.ultrasmedbio.2022.04.218

Cited by 1 publication

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References 36 publications

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“…The low specificity of ultrasound features for classifying thyroid nodules leads to unnecessary FNABs; therefore, new imaging modalities with special attention to anarchical angiogenesis observed in malignancy are of paramount importance in the characterization of nodules. The addition of strain elastography [ 7 , 8 ], shear wave elastography [ 9 , 10 , 11 , 12 , 13 ], and multimodality ultrasound combining B-mode ultrasound, elastography, and contrast-enhanced ultrasound (CEUS) [ 14 ] relatively increased the sensitivity and specificity of ultrasound.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Biomarkers Derived from a Novel Contrast-Free Ultrasound High-Definition Microvessel Imaging for Distinguishing Thyroid Nodules

Kurti

Sabeti

Robinson

et al. 2023

Cancers

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Low specificity in current ultrasound modalities for thyroid cancer detection necessitates the development of new imaging modalities for optimal characterization of thyroid nodules. Herein, the quantitative biomarkers of a new high-definition microvessel imaging (HDMI) were evaluated for discrimination of benign from malignant thyroid nodules. Without the help of contrast agents, this new ultrasound-based quantitative technique utilizes processing methods including clutter filtering, denoising, vessel enhancement filtering, morphological filtering, and vessel segmentation to resolve tumor microvessels at size scales of a few hundred microns and enables the extraction of vessel morphological features as new tumor biomarkers. We evaluated quantitative HDMI on 92 patients with 92 thyroid nodules identified in ultrasound. A total of 12 biomarkers derived from vessel morphological parameters were associated with pathology results. Using the Wilcoxon rank-sum test, six of the twelve biomarkers were significantly different in distribution between the malignant and benign nodules (all p < 0.01). A support vector machine (SVM)-based classification model was trained on these six biomarkers, and the receiver operating characteristic curve (ROC) showed an area under the curve (AUC) of 0.9005 (95% CI: [0.8279,0.9732]) with sensitivity, specificity, and accuracy of 0.7778, 0.9474, and 0.8929, respectively. When additional clinical data, namely TI-RADS, age, and nodule size were added to the features, model performance reached an AUC of 0.9044 (95% CI: [0.8331,0.9757]) with sensitivity, specificity, and accuracy of 0.8750, 0.8235, and 0.8400, respectively. Our findings suggest that tumor vessel morphological features may improve the characterization of thyroid nodules.

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