AIUM Practice Guideline for the Performance of a Breast Ultrasound Examination

Whitman, Gary J.; Rapp, Cindy; Piccoli, Cathy; Gittleman, Mark; Snider, Howard C.; Whitacre, Eric; Paushter, David M.; Angtuaco, Teresita L.; Ackerman, Susan; Crino, Jude P.; Lange, Marie de; Greenbaum, Lennard D.; Gregory, Kimberly D.; Hertzberg, Barbara S.; Hoffenberg, Stephen; Hyde, Charles K.; Jaffe, Richard; Kurtz, Alfred B.; Mastrobattista, Joan M.; Meilstrup, Jon W.; Middleton, William; Robbin, Michelle L.; Rosenberg, Henrietta Kotlus; Wax, Joseph R.; Yeo, Lami

doi:10.7863/jum.2009.28.1.105

Cited by 34 publications

(10 citation statements)

References 9 publications

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“…The B-mode US and SWE acquisition were performed by one of the two radiologists (BO and ML) in center 1 and one radiologist (Shulian Zhuang) in center 2 by using the US system (Aixplorer, SuperSonic Imagine, Aix-en-Provence, France) equipped with a multifrequency linear transducer (SL15–4, SuperSonic Imagine, Aix-en-Provence, France) operating at 4–15 MHz, according to the American Institute of Ultrasound in Medicine practice guidelines ( 3 ). The three radiologists had 15, 5, and 5 years of breast US experience, respectively, and at least 4 years (at least 150 patients per year) of experience of breast SWE.…”

Section: Methodsmentioning

confidence: 99%

“…Breast ultrasonography (US), avoiding the ionizing radiation and the requirement for breast compression, is a valuable supplemental screening tool in women, in particular those with dense breasts and negative mammogram results ( 1 , 2 ). The Breast Imaging Reporting and Data System (BI-RADS) provides a standardized terminology to make an assessment and subsequent recommendation for lesions detected by US ( 3 ). For a lesion with BI-RADS category 3 (probably benign, ≤2% likelihood of malignancy), a short-interval follow-up was recommended instant of immediate biopsy ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification

et al. 2020

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Objective Shear-wave elastography (SWE) can improve the diagnostic specificity of the B-model ultrasonography (US) in breast cancer. However, whether deep learning-based radiomics signatures based on the B-mode US (B-US-RS) or SWE (SWE-RS) could further improve the diagnostic performance remains to be investigated. We aimed to develop the B-US-RS and SWE-RS and determine their performances in classifying breast masses. Materials and Methods This retrospective study included 291 women (mean age ± standard deviation, 40.9 ± 12.3 years) from two centers who had US-visible solid breast masses and underwent biopsy and/or surgical resection between June 2015 and July 2017. B-mode US and SWE images of the 198 masses in 198 patients (training cohort) from center 1 were segmented, respectively, to construct B-US-RS and SWE-RS using the least absolute shrinkage and selection operator regression and tested in an independent validation cohort of 65 masses in 65 patients from center 1 and in an external validation cohort of 28 masses in 28 patients from center 2. The performances of B-US-RS and SWE-RS were assessed using receiver operating characteristic (ROC) analysis and compared with that of radiologist assessment [Breast Imaging Reporting and Data System (BI-RADS)] and quantitative SWE parameters [maximum elasticity ( E max ), mean elasticity ( E mean ), elasticity ratio ( E ratio ), and elastic modulus standard deviation ( E SD )] by using the McNemar test. Results The single best-performing quantitative SWE parameter, E max , had a higher specificity than BI-RADS assessment in the training and independent validation cohorts ( P < 0.001 for both). The areas under the ROC curves (AUCs) of B-US-RS and SWE-RS both were 0.99 (95% CI = 0.99–1.00) in the training cohort, 1.00 (95% CI = 1.00–1.00) in the independent validation cohort, and 1.00 (95% CI = 1.00–1.00) in the external validation cohort. The specificities of B-US-RS and SWE-RS were higher than that of E max in the training ( P < 0.001 for both) and independent validation cohorts ( P = 0.02 for both). Conclusion The B-US-RS and SWE-RS outperformed the quantitative SWE parameters and BI-RADS assessment for classifying breast masses. The integration of the deep learning-based radiomics approach would help improve the classification ability of B-mode US and SWE for breast masses.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification

et al. 2020

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“…Each lesion was scanned with the patient holding their breath for a few seconds in the supine position. For conventional US, a 18L6 linear array transducer (frequency range, 6–18 MHz) was used based on the American Institute of Ultrasound in Medicine practice guideline 17 .…”

Section: Methodsmentioning

confidence: 99%

A Risk Model based on Ultrasound, Ultrasound Elastography, and Histologic Parameters for Predicting Axillary Lymph Node Metastasis in Breast Invasive Ductal Carcinoma

et al. 2017

Sci Rep

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To develop a risk model for predicting axillary lymph node metastasis (LNM) in patients with breast invasive ductal carcinoma (IDCs) using ultrasound (US), US elastography of virtual touch tissue imaging (VTI) and virtual touch tissue imaging & quantification (VTIQ), and histologic parameters. This study included 162 breast IDCs in 162 patients. Univariate and multivariate analyses were used to identify the risk factors and a risk model was created. The results found that 64 (39.5%) of 162 patients had axillary LNMs. The risk score (RS) for axillary LNM was defined as following: RS = 1.3 × (if lesion size ≥20 mm) + 2.6 × (if taller than wide shape) + 2.2 × (if VTI score ≥5) + 3.9 × (if histological grade III) + 1.9 × (if positive C-erbB-2). The rating system was divided into 6 stages (i.e. Stage I, Stage II, Stage III, Stage IV, Stage V, and Stage VI) and the associated risk rates in terms of axillary LNM were 0% (0/19), 6.1% (2/33), 7.7% (3/39), 65.5% (19/29), 92.3% (24/26), and 100% (16/16), respectively. The risk model for axillary LNM established in the study may facilitate subsequent treatment planning and management in patients with breast IDCs.

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“…An expert sonographer with 28 years of experience in breast US conducted the US examinations, according to the American Institute of Ultrasound in Medicine practice guidelines for performing breast US [22]. Breast lesions were localized using the B-mode scanning, and then SWE data were acquired prior to biopsy.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic features of quantitative comb-push shear elastography for breast lesion differentiation

et al. 2017

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BackgroundLesion stiffness measured by shear wave elastography has shown to effectively separate benign from malignant breast masses. The aim of this study was to evaluate different aspects of Comb-push Ultrasound Shear Elastography (CUSE) performance in differentiating breast masses.MethodsWith written signed informed consent, this HIPAA- compliant, IRB approved prospective study included patients from April 2014 through August 2016 with breast masses identified on conventional imaging. Data from 223 patients (19–85 years, mean 59.93±14.96 years) with 227 suspicious breast masses identifiable by ultrasound (mean size 1.83±2.45cm) were analyzed. CUSE was performed on all patients. Three regions of interest (ROI), 3 mm in diameter each, were selected inside the lesion on the B-mode ultrasound which also appeared in the corresponding shear wave map. Lesion elasticity values were measured in terms of the Young’s modulus. In correlation to pathology results, statistical analyses were performed.ResultsPathology revealed 108 lesions as malignant and 115 lesions as benign. Additionally, 4 lesions (BI-RADS 2 and 3) were considered benign and were not biopsied. Average lesion stiffness measured by CUSE resulted in 84.26% sensitivity (91 of 108), 89.92% specificity (107 of 119), 85.6% positive predictive value, 89% negative predictive value and 0.91 area under the curve (P<0.0001). Stiffness maps showed spatial continuity such that maximum and average elasticity did not have significantly different results (P > 0.21).ConclusionCUSE was able to distinguish between benign and malignant breast masses with high sensitivity and specificity. Continuity of stiffness maps allowed for choosing multiple quantification ROIs which covered large areas of lesions and resulted in similar diagnostic performance based on average and maximum elasticity. The overall results of this study, highlights the clinical value of CUSE in differentiation of breast masses based on their stiffness.

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AIUM Practice Guideline for the Performance of a Breast Ultrasound Examination

Cited by 34 publications

References 9 publications

Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification

Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification

A Risk Model based on Ultrasound, Ultrasound Elastography, and Histologic Parameters for Predicting Axillary Lymph Node Metastasis in Breast Invasive Ductal Carcinoma

Diagnostic features of quantitative comb-push shear elastography for breast lesion differentiation

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