Purpose: Evaluation of an automated breast ultrasound system (ABUS) regarding the detection and classification of breast lesions according to BI-RADS.

Materials And Methods: Women were selected for the study who had unclear findings in breast diagnosis performed elsewhere (palpation, sonography or mammography) and who were referred for further work-up. All patients received a hand-held ultrasonography (HHUS) with a 13 MHz transducer, clinical examination and mammography of both breasts. Additionally, the affected breast received the ABUS (SomoVuTM, U-Systems, Inc., San Jose, CA, USA; EC Representative: Siemens, Erlangen, Germany) which was performed with an 8 MHz transducer. Five radiologists independently evaluated the ABUS images regarding lesion detectability. All detected lesions were classified according to BI-RADS assessment. The examiners had no knowledge of the patients' clinical examination or of the result of the mammography or the HHUS. Results of the ABUS were compared to HHUS.

Results: 35 women were included in the study. 25 BI-RADS 4 or 5 lesions had further histological (n = 23) or cytological (n = 2) work-up which revealed 13 malignant and 12 benign findings. The size of all lesions ranged from 6 to 32 mm (median 14 mm). With the ABUS all examiners detected 29 to 30 lesions while HHUS revealed 30 lesions. One suspicious area in HHUS was not reported by any of the five examiners with the ABUS. Histology of this area revealed mastopathic disease. No benign lesion was classified as BI-RADS 5 with the ABUS or HHUS. All breast cancers were found with the ABUS by all examiners and correctly classified as BI-RADS 4 or 5. There was good agreement regarding BI-RADS classification of HHUS and ABUS for the five different examiners with Kappa values between 0.83 and 0.87.

Conclusion: These preliminary results show that the ABUS allows detection of solid and cystic lesions and their BI-RADS classification with a high reliability in a selected patient group.

Download full-text PDF

Source
http://dx.doi.org/10.1055/s-2008-1027563DOI Listing

Publication Analysis

Top Keywords

classified bi-rads
12
abus examiners
12
abus
10
automated breast
8
breast ultrasound
8
mhz transducer
8
clinical examination
8
detected lesions
8
hhus abus
8
bi-rads classification
8

Similar Publications

This study aimed to develop a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression (LR) model using quantitative imaging features from Shear Wave Elastography (SWE) and Contrast-Enhanced Ultrasound (CEUS) to assess the malignancy risk of BI-RADS 4 breast lesions (BLs). The features predictive of malignancy in the LASSO analysis were used to construct a nomogram. Female patients (n = 111) with BI-RADS 4 BLs detected via routine ultrasound at Ma'anshan People's Hospital underwent SWE, CEUS, and histopathological examinations were enrolled in this study.

View Article and Find Full Text PDF

A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade.

Life (Basel)

December 2024

Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, San Andres Cholula 72840, Mexico.

Identifying breast masses is relevant in early cancer detection. Automatic identification using computational methods helps assist medical experts with this task. Although high values have been reported in breast mass classification from digital mammograms, most results have focused on a general benign/malignant classification.

View Article and Find Full Text PDF

Objective: This study develops a BI-RADS-like scoring system for vascular microcalcifications in mammographies, correlating breast arterial calcification (BAC) in a mammography with coronary artery calcification (CAC), and specifying differences between microcalcifications caused by BAC and microcalcifications potentially associated with malignant disease.

Materials And Methods: This retrospective single-center cohort study evaluated 124 consecutive female patients (with a median age of 57 years). The presence of CAC was evaluated based on the Agatston score obtained from non-enhanced coronary computed tomography, and the calcifications detected in the mammography were graded on a four-point Likert scale, with the following criteria: (1) no visible or sporadically scattered microcalcifications, (2) suspicious microcalcification not distinguishable from breast arterial calcification, (3) minor breast artery calcifications, and (4) major breast artery calcifications.

View Article and Find Full Text PDF

MIC: Breast Cancer Multi-label Diagnostic Framework Based on Multi-modal Fusion Interaction.

J Imaging Inform Med

January 2025

School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.

The automated diagnosis of low-resolution and difficult-to-recognize breast ultrasound images through multi-modal fusion holds significant clinical value. However, prevailing fusion methods predominantly rely on image modalities, neglecting the textual pathology information, and only benign and malignant diagnosis of breast tumors is not satisfying for clinical applications. Consequently, this paper proposes a novel multi-modal fusion interactive diagnostic framework, termed the MIC framework, to achieve the multi-label classification of breast cancer, namely benign-malignant classification and breast imaging reporting and data system (BI-RADS) 3, 4a, 4b, 4c, and 5 gradings.

View Article and Find Full Text PDF

Objective: To analyse the parameters of shear wave elastography (SWE) and contrast-enhanced ultrasound (CEUS) in breast non-mass-like lesions (NMLs) and to evaluate the added diagnostic value of SWE and CEUS when combined with B-mode ultrasound (US) for differentiating NMLs.

Methods: A total of 118 NMLs from 115 patients underwent US, SWE, and CEUS examinations. The SWE parameter with the highest areas under the receiver operating characteristic (ROC) curves (Az) and independent variables of CEUS obtained by logistic regression were used to adjust the BI-RADS-US (Breast Imaging Reporting and Data System for Ultrasound) classification.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!