Simple Summary Low specificity of ultrasound in detecting thyroid cancer warrants the development of new noninvasive modalities for the optimal characterization of thyroid nodules. Here, we present a new ultrasound-based technique, high-definition microvasculature imaging (HDMI) that provides quantitative measures of tumor microvasculature morphological features as new imaging biomarkers. This technique utilizes vessel enhancement filtering, morphological filtering, and vessel segmentation, which enable extraction of vessel morphological features including tortuosity, vessel density, diameter, Murray's deviation, microvessel fractal dimension, bifurcation angle, number of branch points, and vessel segments. Without the help of contrast agents, through the utilization of HDMI on patients with suspicious thyroid nodules, we were able to resolve tumor microvessels at size scales of a few hundred microns. We further showed that analysis of tumor vessel morphological parameters could detect thyroid malignancy with high sensitivity and specificity. These findings provide a translational rationale for the clinical implementation of quantitative HDMI for thyroid cancer detection. 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.

Quantitative Biomarkers Derived from a Novel Contrast-Free Ultrasound High-Definition Microvessel Imaging for Distinguishing Thyroid Nodules / Kurti, Melisa; Sabeti, Soroosh; Robinson, Kathryn A; Scalise, Lorenzo; Larson, Nicholas B; Fatemi, Mostafa; Alizad, Azra. - In: CANCERS. - ISSN 2072-6694. - 15:6(2023), p. 1888. [10.3390/cancers15061888]

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

Kurti, Melisa;Scalise, Lorenzo;
2023-01-01

Abstract

Simple Summary Low specificity of ultrasound in detecting thyroid cancer warrants the development of new noninvasive modalities for the optimal characterization of thyroid nodules. Here, we present a new ultrasound-based technique, high-definition microvasculature imaging (HDMI) that provides quantitative measures of tumor microvasculature morphological features as new imaging biomarkers. This technique utilizes vessel enhancement filtering, morphological filtering, and vessel segmentation, which enable extraction of vessel morphological features including tortuosity, vessel density, diameter, Murray's deviation, microvessel fractal dimension, bifurcation angle, number of branch points, and vessel segments. Without the help of contrast agents, through the utilization of HDMI on patients with suspicious thyroid nodules, we were able to resolve tumor microvessels at size scales of a few hundred microns. We further showed that analysis of tumor vessel morphological parameters could detect thyroid malignancy with high sensitivity and specificity. These findings provide a translational rationale for the clinical implementation of quantitative HDMI for thyroid cancer detection. 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.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/317732
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