Novel ultrasound elastography system for multifocal breast cancer assessment

2012 ◽  
Author(s):  
Shadi Shavakh ◽  
Aaron Fenster ◽  
Abbas Samani
Keyword(s):  
Breast Cancer ◽  
Ultrasound Elastography ◽  
Multifocal Breast Cancer

Realtime Ultrasound Elastography Technique for Multifocal Breast Cancer Assessment

2010 ◽  
Author(s):  
Shadi Shavakh ◽  
Aaron Fenster ◽  
Abbas Samani
Keyword(s):  
Breast Cancer ◽  
Treatment Plan ◽  
Cost Effective ◽  
Ultrasound Elastography ◽  
Average Error ◽  
Appropriate Treatment ◽  
Multifocal Breast Cancer ◽  
Diagnosis And Classification ◽  
Phantom Studies

Early diagnosis and classification of breast cancer is a critical step in choosing appropriate treatment plan. An ultrasound (US) elastography method for unifocal and multifocal breast cancer is presented. While this technique uses full inversion approach, it is cost-effective, fast, and expected to be more sensitive and specific than conventional US based elastography methods. This technique is capable of imaging absolute Young’s modulus (YM) of the tumour in real-time fashion, in contrast with other conventional elastography techniques that image relative elastic modulus off-line. To validate the proposed technique, numerical and tissue mimicking phantom studies were conducted. In the tissue mimicking study, a block shape gelatine-agar phantom was constructed with a cylindrical inclusion located deep inside the phantom. Results obtained from this study show accurate reconstruction of the YM with average error of less than 3%. The numerical phantom study has been extended for multifocal cases with average errors less than 6%.

The Accuracy of Combined Versus Largest Diameter in Staging Multifocal Breast Cancer

2007 ◽  
Vol 204 (2) ◽  
pp. 282-285 ◽  
Author(s):  
Brendan J. O’Daly ◽  
Karl J. Sweeney ◽  
Paul F. Ridgway ◽  
Cecily Quinn ◽  
Enda W.M. McDermott ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Multifocal Breast Cancer

scholarly journals The Utility of Mammi-Pet in Multifocal Breast Cancer

2016 ◽  
Vol 01 (04) ◽  
Author(s):  
Alejandra de Andres Gomez ◽  
Raul Sanchez Jurado ◽  
Carlos Fuster Diana
Keyword(s):  
Breast Cancer ◽  
Multifocal Breast Cancer

Multifocal breast cancer and survival: Each focus does matter particularly for larger tumours

2010 ◽  
Vol 46 (11) ◽  
pp. 1990-1996 ◽  
Author(s):  
John Boyages ◽  
Upali W. Jayasinghe ◽  
Nathan Coombs
Keyword(s):  
Breast Cancer ◽  
Multifocal Breast Cancer

Developing and validating deep learning-based heterogeneous model to improve diagnostic performance of ultrasound elastography for axillary lymph node metastasis of early breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12583-e12583
Author(s):  
Jian Li ◽  
Cai Nian ◽  
Xie Ze-Ming ◽  
Zhou Jingwen ◽  
Huang Kemin
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Early Breast Cancer ◽  
Predictive Value ◽  
Axillary Lymph Node ◽  
Diagnostic Performance ◽  
Ultrasound Elastography ◽  
Axillary Lymph ◽  
Heterogeneous Model

e12583 Background: To improve the performance of ultrasound (US) for diagnosing metastatic axillary lymph node (ALN), machine learning was used to reveal the inherently medical hints from ultrasonic images and assist pre-treatment evaluation of ALN for patients with early breast cancer. Methods: A total of 214 eligible patients with 220 breast lesions, from whom 220 target ALNs of ipsilateral axillae underwent ultrasound elastography (UE), were prospectively recruited. Based on feature extraction and fusion of B-mode and shear wave elastography (SWE) images of 140 target ALNs using radiomics and deep learning, with reference to the axillary pathological evaluation from training cohort, a proposed deep learning-based heterogeneous model (DLHM) was established and then validated by a collection of B-mode and SWE images of 80 target ALNs from testing cohort. Performance was compared between UE based on radiological criteria and DLHM in terms of areas under the receiver operating characteristics curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value for diagnosing ALN metastasis. Results: DLHM achieved an excellent performance for both training and validation cohorts. In the prospectively testing cohort, DLHM demonstrated the best diagnostic performance with AUC of 0.911(95% confidence interval [CI]: 0.826, 0.963) in identifying metastatic ALN, which significantly outperformed UE in terms of AUC (0.707, 95% CI: 0.595, 0.804, P<0.001). Conclusions: DLHM provides an effective, accurate and non-invasive preoperative method for assisting the diagnosis of ALN metastasis in patients with early breast cancer.[Table: see text]

scholarly journals Bilateral and Multifocal Breast Cancer

1996 ◽  
Vol 3 (3) ◽  
pp. 258-266 ◽  
Author(s):  
Peter J. Dawson
Keyword(s):  
Breast Cancer ◽  
Multifocal Breast Cancer

scholarly journals Tumor size and survival in multicentric and multifocal breast cancer

The Breast ◽  
2011 ◽  
Vol 20 (3) ◽  
pp. 259-263 ◽  
Author(s):  
Angela Rezo ◽  
Jane Dahlstrom ◽  
Bruce Shadbolt ◽  
Karl Rodins ◽  
Yanping Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Size ◽  
Multifocal Breast Cancer
Sign in / Sign up

Export Citation Format

Share Document