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 32 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

159

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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

159

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“…Incorporating CEUS with BMUS into a scoring system could improve the accuracy of breast tumor diagnosis . Dual‐modal US images (ie, real‐time elastography and BMUS) have the ability to evaluate axillary lymph node metastasis in patients with breast cancer . However, a quantitative analysis of these multimodal US images for breast tumor diagnosis has not been reported.…”

mentioning

confidence: 99%

Radiomics With Attribute Bagging for Breast Tumor Classification Using Multimodal Ultrasound Images

Liu

Zhang

et al. 2019

J of Ultrasound Medicine

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Objectives We aimed to develop radiomics with attribute bagging, which leverages multimodal ultrasound (US) images to improve the classification accuracy of breast tumors. Methods A retrospective study was conducted. B‐mode US, shear wave elastographic, and contrast‐enhanced US images of 178 patients with 181 tumors (67 malignant and 114 benign) were included. Radiomics with attribute bagging consisted of extraction of 1226 radiomic features and analysis of them with attribute bagging. Histologic examination results acted as the reference standard. Radiomics with several feature selection algorithms were used for comparison. Cross‐validation and a holdout test were performed to evaluate their performances. Results The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of radiomics with attribute bagging with the multimodal US images were 84.12%, 92.86%, 78.80%, and 0.919, respectively, exceeding all the comparison methods. Conclusions Radiomics with attribute bagging combined with multimodal US images has the potential to be used for accurate diagnosis of breast tumors in the clinic.

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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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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 32 publications

References 28 publications

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

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

Radiomics With Attribute Bagging for Breast Tumor Classification Using Multimodal Ultrasound Images

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

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