A Quantitative Ultrasound-Based Multi-Parameter Classifier for Breast Masses

Abstract

This manuscript reports preliminary results obtained by combining estimates of two or three (among seven) quantitative ultrasound (QUS) parameters in a model-free, multi-parameter classifier to differentiate breast carcinomas from fibroadenomas (the most common benign solid tumor). Forty-three subjects scheduled for core biopsy of a suspicious breast mass were recruited. Radiofrequency echo signal data were acquired using clinical breast ultrasound systems equipped with linear array transducers. The Reference Phantom Method was used to obtain systemindependent estimates of the specific attenuation (ATT), the average backscatter coefficients (ABSC), the effective scatterer diameter (ESD) and an effective scatterer diameter heterogeneity index (ESDHI) over regions of interest within each mass. In addition, the envelope amplitude signal-to-noise ratio (SNR), the Nakagami shape parameter, m, and the maximum collapsed average (maxCA) of the generalized spectrum were also computed. Classification was performed using the minimum Mahalanobis distance to the centroids of the training classes and tested against biopsy results. Classification performance was evaluated with the area under the receiver-operating characteristic (ROC) curve. The best performance with a two-parameter classifier used the ESD and ESDHI and resulted in an area under the ROC curve of 0.98 [0.95-1.00, 95% confidence interval]. Classification performance improved with three parameters (ATT, ESD, and ESDHI) yielding an area under the ROC curve of 0.999 [0.995-1.000].These results suggest that system independent QUS parameters, when combined in a model-free classifier, are a promising tool to characterize breast tumors. A larger study is needed to further test this idea.

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

“…QUS is relatively inexpensive compared with MRI, CT, or PET, and other advantages include system portability and mitigating operator and system dependence during image acquisition and processing. There is current work to validate QUS-radiomics in the long term, particularly in breast cancer [42][43][44]. Novel QUS imaging techniques to attain imaging biomarkers aim for objective, reproducible, and have the high potential for AI-driven automation and interpretation.…”

Combination of shear wave elastography and BI-RADS in identification of solid breast masses

“…It suggests that all malignant masses present more than one characteristic of malignant masses; moreover, some benign masses also show malignant signs such as microcalcification, which may be caused by secondary calcification in inflammatory lesions, so careful screening should be conducted. Although two-dimensional ultrasonography has many advantages, it needs to be supplemented by other methods to confirm the nature of masses due to the fact that the malignant masses have unclear morphological characteristics in the early-stage [17][18][19][20]. The advancing of ultrasound technology allows for a wide application of Color Doppler in numerous diseases; this approach has a high sensitivity to blood flow and can reflect the the hemodynamics of the lesions, with a promising clinical application prospect.…”

“…( 5) Adler grading of blood flow. Adler grading was used to classify blood flow detected into four grades: Grade 0: no blood flow; Grade I: a small amount of dot-like blood flow; Grade II: one longer blood flow through the mass, or 3 dot-like blood blood flow signals; Grade III: more than 4 dot-like blood blood flow signals [9][10][11][12].…”

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

“…Early diagnosis and treatment of breast cancer can significantly improve the survival rate and quality of life [ 2 ]. Conventional ultrasound (US) plays an important role in breast cancer screening and the differential diagnosis of breast lesions [ 3 ]. Breast lesions detected by US can be classified into seven categories (categories 0–6) according to the fifth edition of the Breast Imaging Reporting and Data System (BI-RADS) [ 4 ].…”