Cristina Caresio1, Marco Caballo1,2, Maurilio Deandrea3, Roberto Garberoglio4, Alberto Mormile3, Ruth Rossetto5, Paolo Limone3, Filippo Molinari1. 1. Biolab, Department of Electronics and Telecommunication, Politecnico di Torino, Turin, Italy. 2. Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, PO Box 9101, Nijmegen, 6500 HB, The Netherlands. 3. Endocrinology Section, "Umberto I" Hospital, Ordine Mauriziano di Torino, University of Turin, Turin, Italy. 4. Fondazione Scientifica Mauriziana ONLUS, Turin, Italy. 5. Division of Endocrinology, Diabetology and Metabolism, Department of Medical Sciences, University of Turin, Turin, Italy.
Abstract
PURPOSE: To perform a comparative quantitative analysis of Power Doppler ultrasound (PDUS) and Contrast-Enhancement ultrasound (CEUS) for the quantification of thyroid nodules vascularity patterns, with the goal of identifying biomarkers correlated with the malignancy of the nodule with both imaging techniques. METHODS: We propose a novel method to reconstruct the vascular architecture from 3-D PDUS and CEUS images of thyroid nodules, and to automatically extract seven quantitative features related to the morphology and distribution of vascular network. Features include three tortuosity metrics, the number of vascular trees and branches, the vascular volume density, and the main spatial vascularity pattern. Feature extraction was performed on 20 thyroid lesions (ten benign and ten malignant), of which we acquired both PDUS and CEUS. MANOVA (multivariate analysis of variance) was used to differentiate benign and malignant lesions based on the most significant features. RESULTS: The analysis of the extracted features showed a significant difference between the benign and malignant nodules for both PDUS and CEUS techniques for all the features. Furthermore, by using a linear classifier on the significant features identified by the MANOVA, benign nodules could be entirely separated from the malignant ones. CONCLUSIONS: Our early results confirm the correlation between the morphology and distribution of blood vessels and the malignancy of the lesion, and also show (at least for the dataset used in this study) a considerable similarity in terms of findings of PDUS and CEUS imaging for thyroid nodules diagnosis and classification.
PURPOSE: To perform a comparative quantitative analysis of Power Doppler ultrasound (PDUS) and Contrast-Enhancement ultrasound (CEUS) for the quantification of thyroid nodules vascularity patterns, with the goal of identifying biomarkers correlated with the malignancy of the nodule with both imaging techniques. METHODS: We propose a novel method to reconstruct the vascular architecture from 3-D PDUS and CEUS images of thyroid nodules, and to automatically extract seven quantitative features related to the morphology and distribution of vascular network. Features include three tortuosity metrics, the number of vascular trees and branches, the vascular volume density, and the main spatial vascularity pattern. Feature extraction was performed on 20 thyroid lesions (ten benign and ten malignant), of which we acquired both PDUS and CEUS. MANOVA (multivariate analysis of variance) was used to differentiate benign and malignant lesions based on the most significant features. RESULTS: The analysis of the extracted features showed a significant difference between the benign and malignant nodules for both PDUS and CEUS techniques for all the features. Furthermore, by using a linear classifier on the significant features identified by the MANOVA, benign nodules could be entirely separated from the malignant ones. CONCLUSIONS: Our early results confirm the correlation between the morphology and distribution of blood vessels and the malignancy of the lesion, and also show (at least for the dataset used in this study) a considerable similarity in terms of findings of PDUS and CEUS imaging for thyroid nodules diagnosis and classification.
Authors: Juanjuan Gu; Redouane Ternifi; Mostafa Fatemi; Azra Alizad; Nicholas B Larson; Jodi M Carter; Judy C Boughey; Daniela L Stan; Robert T Fazzio Journal: Breast Cancer Res Date: 2022-03-05 Impact factor: 8.408
Authors: Redouane Ternifi; Yinong Wang; Eric C Polley; Robert T Fazzio; Mostafa Fatemi; Azra Alizad Journal: IEEE Trans Med Imaging Date: 2021-11-30 Impact factor: 10.048