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Breast ultrasound

About: Breast ultrasound is a research topic. Over the lifetime, 1804 publications have been published within this topic receiving 30054 citations. The topic is also known as: Ultrasonography, Mammary.


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Journal ArticleDOI
TL;DR: Sonography can be used to accurately classify some solid lesions as benign, allowing imaging follow-up rather than biopsy, and this distinction could be definite enough to obviate biopsy.
Abstract: PURPOSE: To determine whether sonography could help accurately distinguish benign solid breast nodules from indeterminate or malignant nodules and whether this distinction could be definite enough to obviate biopsy. MATERIALS AND METHODS: Seven hundred fifty sonographically solid breast nodules were prospectively classified as benign, indeterminate, or malignant. Benign nodules had no malignant characteristics and had either intense homogeneous hyperechogenicity or a thin echogenic pseudocapsule with an ellipsoid shape or fewer than four gentle lobulations. Sonographic classifications were compared with biopsy results. The sensitivity, specificity, and negative and positive predictive values of the classifications were calculated. RESULTS: Benign histologic features were found in 625 (83%) lesions; malignant histologic features, in 125 (17%). Of benign lesions, 424 had been prospectively classified as benign. Two lesions classified as benign were found to be malignant at biopsy. Thus, the classification s...

1,683 citations

Journal ArticleDOI
14 May 2008-JAMA
TL;DR: The diagnostic yield, sensitivity, specificity, and diagnostic accuracy (assessed by the area under the receiver operating characteristic curve) of combined mammography plus ultrasound vs mammography alone and the positive predictive value of biopsy recommendations for mammographyplus ultrasound vs Mammography alone are compared.
Abstract: Context Screening ultrasound may depict small, node-negative breast cancers not seen on mammography. Objective To compare the diagnostic yield, defined as the proportion of women with positive screen test results and positive reference standard, and performance of screening with ultrasound plus mammography vs mammography alone in women at elevated risk of breast cancer. Design, Setting, and Participants From April 2004 to February 2006, 2809 women, with at least heterogeneously dense breast tissue in at least 1 quadrant, were recruited from 21 sites to undergo mammographic and physician-performed ultrasonographic examinations in randomized order by a radiologist masked to the other examination results. Reference standard was defined as a combination of pathology and 12-month follow-up and was available for 2637 (96.8%) of the 2725 eligible participants. Main Outcome Measures Diagnostic yield, sensitivity, specificity, and diagnostic accuracy (assessed by the area under the receiver operating characteristic curve) of combined mammography plus ultrasound vs mammography alone and the positive predictive value of biopsy recommendations for mammography plus ultrasound vs mammography alone. Results Forty participants (41 breasts) were diagnosed with cancer: 8 suspicious on both ultrasound and mammography, 12 on ultrasound alone, 12 on mammography alone, and 8 participants (9 breasts) on neither. The diagnostic yield for mammography was 7.6 per 1000 women screened (20 of 2637) and increased to 11.8 per 1000 (31 of 2637) for combined mammography plus ultrasound; the supplemental yield was 4.2 per 1000 women screened (95% confidence interval [CI], 1.1-7.2 per 1000; P = .003 that supplemental yield is 0). The diagnostic accuracy for mammography was 0.78 (95% CI, 0.67-0.87) and increased to 0.91 (95% CI, 0.84-0.96) for mammography plus ultrasound (P = .003 that difference is 0). Of 12 supplemental cancers detected by ultrasound alone, 11 (92%) were invasive with a median size of 10 mm (range, 5-40 mm; mean [SE], 12.6 [3.0] mm) and 8 of the 9 lesions (89%) reported had negative nodes. The positive predictive value of biopsy recommendation after full diagnostic workup was 19 of 84 for mammography (22.6%; 95% CI, 14.2%-33%), 21 of 235 for ultrasound (8.9%, 95% CI, 5.6%-13.3%), and 31 of 276 for combined mammography plus ultrasound (11.2%; 95% CI. 7.8%-15.6%). Conclusions Adding a single screening ultrasound to mammography will yield an additional 1.1 to 7.2 cancers per 1000 high-risk women, but it will also substantially increase the number of false positives. Trial Registration clinicaltrials.gov Identifier: NCT00072501

1,251 citations

Journal ArticleDOI
TL;DR: M mammography alone, and also mammography combined with breast ultrasound, seems insufficient for early diagnosis of breast cancer in women who are at increased familial risk with or without documented BRCA mutation, but if MRI is used for surveillance, diagnosis of intraductal and invasive familial or hereditary cancer is achieved with a significantly higher sensitivity and at a more favorable stage.
Abstract: Purpose To compare the effectiveness of mammography, breast ultrasound, and magnetic resonance imaging (MRI) for surveillance of women at increased familial risk for breast cancer (lifetime risk of 20% or more). Patients and Methods We conducted a surveillance cohort study of 529 asymptomatic women who, based on their family history and/or mutational analysis, were suspected or proven to carry a breast cancer susceptibility gene (BRCA). A total of 1,542 annual surveillance rounds were completed with a mean follow-up of 5.3 years. Diagnostic accuracies of the three imaging modalities used alone or in different combinations were compared. Results Forty-three breast cancers were identified in the total cohort (34 invasive, nine ductal carcinoma-in-situ). Overall sensitivity of diagnostic imaging was 93% (40 of 43 breast cancers); overall node-positive rate was 16%, and one interval cancer occurred (one of 43 cancers, or 2%). In the analysis by modality, sensitivity was low for mammography (33%) and ultrasoun...

1,050 citations

Journal ArticleDOI
TL;DR: The Society of Breast Imaging and the Breast Imaging Commission of the ACR are issuing these recommendations to provide guidance to patients and clinicians on the use of imaging to screen for breast cancer.
Abstract: Screening for breast cancer with mammography has been shown to decrease mortality from breast cancer, and mammography is the mainstay of screening for clinically occult disease. Mammography, however, has wellrecognizedlimitations,andrecently,otherimagingincludingultrasoundandmagneticresonanceimaginghave been used as adjunctive screening tools, mainly for women who may be at increased risk for the development of breast cancer. The Society of Breast Imaging and the Breast Imaging Commission of the ACR are issuing these recommendations to provide guidance to patients and clinicians on the use of imaging to screen for breast cancer. Wherever possible, the recommendations are based on available evidence. Where evidence is lacking, the recommendations are based on consensus opinions of the fellows and executive committee of the Society of Breast Imaging and the members of the Breast Imaging Commission of the ACR.

693 citations

Journal ArticleDOI
TL;DR: This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet.
Abstract: Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

564 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023128
2022267
2021187
2020152
2019112
2018146