Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound

Zhang, Qi; Suo, Jingfeng; Chang, Wan-Ying; Shi, Jun; Chen, Man

doi:10.1016/j.ejrad.2017.07.027

Cited by 33 publications

(26 citation statements)

References 28 publications

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“…It utilized the kernel functions to map the original data into the higher dimensional space to find the decision hyperplane. SVM is widely applied in the analysis of ultrasound images [ 15 , 47 – 49 ]. SVM can perform well in both of small dataset and large dataset.…”

Section: Traditional Ultrasound Cad Systemmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

165

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The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

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Section: Traditional Ultrasound Cad Systemmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

165

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“…The common two practices are to directly evaluate the hard area ratio or to convert the hard area ratio to an elasticity score to assess the stiffness of lymph nodes. In the qualitative elastography meta-analysis, four studies directly evaluated the hard area ratio (10,12,25,26), while four others converted the hard area ratio as the elasticity score (23,24,27,28). We found that the optimal cutoff values of the hard area ratio were all 50%, and the optimal cutoff values of elasticity score were all 3.…”

Section: Discussionmentioning

confidence: 91%

“…Therefore, the seven studies that reported quantitative elastography measurements were included in the quantitative elastography meta-analysis (8,(17)(18)(19)(20)(21)(22). The 11 studies that reported qualitative elastography measurements were included in the qualitative elastography meta-analysis (8)(9)(10)(11)(12)(23)(24)(25)(26)(27)(28). The inclusion and exclusion processes are summarized in Figure 1.…”

Section: Search Resultsmentioning

confidence: 99%

Diagnostic Performance of Quantitative and Qualitative Elastography for Axillary Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta-Analysis

Huang

et al. 2020

Front. Oncol.

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Background: Studies have shown inconsistent results regarding the diagnostic performance of ultrasound elastography for axillary lymph node metastasis (ALNM) in breast cancer. This meta-analysis aimed to estimate the diagnostic performance of ultrasound elastography (divided into quantitative and qualitative elastography) for ALNM in patients with breast cancer. Methods: The PubMed and Embase databases were searched for eligible studies exploring the diagnostic performance of ultrasound elastography for ALNM in patients with breast cancer. The included studies were divided into quantitative and qualitative elastography groups to perform separate meta-analyses. The diagnostic performance was investigated with pooled sensitivity and specificity and diagnostic odds ratio (DOR) using a bivariate mixed-effects regression model. A summary receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated. Results: Seven and 11 studies were included in the quantitative and qualitative elastography meta-analyses, respectively. The pooled sensitivity and specificity, DOR, and AUC with their corresponding 95% confidence intervals were 0.82 (0.75, 0.87), 0.88 (0.78, 0.93), 33 (13, 83), and 0.89 (0.86, 0.91), respectively, for quantitative elastography and 0.81 (0.69, 0.89), 0.92 (0.79, 0.97), 46 (12, 181), and 0.92 (0.89, 0.94), respectively, for qualitative elastography. No significant publication bias existed. Fagan plots demonstrated good clinical utility. However, substantial heterogeneity existed among studies. Study design, measurement, and reference standard served as potential sources of heterogeneity for quantitative studies, which were measurement and reference standard for qualitative studies. Conclusions: Both quantitative and qualitative elastography seem to be feasible, non-invasive diagnostic tools for ALNM in breast cancer. Nevertheless, the results Huang et al. Meta-Analysis of Elastography for ALNM must be interpreted carefully, paying attention to heterogeneity issues, especially for quantitative elastography studies.

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“…CAD model can also be used to classify benign and metastatic lymph nodes in patients with breast tumor. Zhang et al[13] proposed a computer-assisted method through dual-modal features extracted from real-time elastography (RTE) and B-mode ultrasound. With the assistance of computer, five morphological features describing the hilum, size, shape, and echogenic uniformity of a lymph node were extracted from B-mode ultrasound, and three elastic features consisting of hard area ratio, strain ratio, and coeﬃcient of variance were extracted from RTE.…”

Section: Feature Extractionmentioning

confidence: 99%

Artificial intelligence in breast ultrasound

Wu¹,

Zhou²,

Xu³

et al. 2019

WJR

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Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.

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Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound

Cited by 33 publications

References 28 publications

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Diagnostic Performance of Quantitative and Qualitative Elastography for Axillary Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta-Analysis

Artificial intelligence in breast ultrasound

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