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 Vol. 25; no. 3; pp. 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:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01-09-2022 Springer Nature B.V
Subjects:	Biopsy Breast Breast - diagnostic imaging Female Humans Image segmentation Image-Guided Biopsy Lipids Machine Learning Medicine Medicine & Public Health Microspheres Performance evaluation Performance prediction Prediction models Radiomics Retrospective Studies Software Sulfur hexafluoride Ultrasonic imaging Ultrasound Breast masses Malignancy Machine learning Radiomics CEUS
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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 < 0.05 in differentiating between benign from malignant masses. No feature selection was performed. Predictive models based on AdaBoost reported an AUC = 0.72 95% CI (0.56, 0.89), followed by Random Forest with an AUC = 0.71 95% CI (0.56, 0.87). Conclusions CEUS based texture metrics can distinguish between benign and malignant breast masses, which can, in turn, lead to reduced unnecessary breast biopsies.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1876-7931 1971-3495 1876-7931
DOI:	10.1007/s40477-021-00651-2