Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics

Aims We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses. Methods 131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren...

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Bibliographic Details
Published in:Journal of ultrasound 2022-09, Vol.25 (3), p.699-708
Main Authors: Varghese, Bino A., Lee, Sandy, Cen, Steven, Talebi, Amir, Mohd, Passant, Stahl, Daniel, Perkins, Melissa, Desai, Bhushan, Duddalwar, Vinay A., Larsen, Linda H.
Format: Article
Language:English
Subjects:
Biopsy
Breast
Image segmentation
Lipids
Machine learning
Medicine
Medicine & Public Health
Microspheres
Performance evaluation
Performance prediction
Prediction models
Radiomics
Software
Sulfur hexafluoride
Ultrasonic imaging
Ultrasound
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Summary:Aims We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses. Methods 131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren lipid microsphere or sulfur hexafluoride lipid-type A microspheres) prior to ultrasound-guided biopsies were retrospectively identified. Post biopsy pathology showed 115 benign and 16 malignant masses. From the cine clip of the CEUS exams obtained using the built-in GE scanner software, breast masses and adjacent normal tissue were then manually segmented using the ImageJ software. One frame representing each of the four phases: precontrast, early, peak, and delay enhancement were selected post segmentation from each CEUS clip. 112 radiomic metrics were extracted from each segmented tissue normalized breast mass using custom Matlab ® code. Linear and nonlinear machine learning (ML) methods were used to build the prediction model to distinguish benign from malignant masses. tenfold cross-validation evaluated model performance. Area under the curve (AUC) was used to quantify prediction accuracy. Results Univariate analysis found 35 (38.5%) radiomic variables with p  
ISSN:1876-7931
1971-3495
1876-7931
DOI:10.1007/s40477-021-00651-2