Quantitative Ultrasound Image Analysis of Axillary Lymph Nodes to Diagnose Metastatic Involvement in Breast Cancer

Coronado-Gutiérrez, David; Santamaría, Gorane; Ganau, Sergi; Bargalló, Xavier; Orlando, Stefania; Oliva-Brañas, M. Eulalia; Perez‐Moreno, Alvaro; Burgos-Artizzu, Xavier P.

doi:10.1016/j.ultrasmedbio.2019.07.413

Cited by 26 publications

(23 citation statements)

References 18 publications

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“…First, the breast mass was scanned, and features such as its location and size were recorded. Next, the axilla was explored, and lymph nodes that showed the disappearance of lymphatic portals or cortical thickness ≥ 3 mm under US were defined as suspicious lymph nodes and considered SLN metastases [16,17].…”

Section: Conventional Ultrasoundmentioning

confidence: 99%

Contrast-Enhanced Ultrasound for Precise Sentinel Lymph Node Biopsy in Women with Early Breast Cancer: A Preliminary Study

et al. 2021

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Background: Sentinel lymph node biopsy (SLNB), as a common method for axillary staging of early breast cancer, has gradually attracted people’s attention to the false-negative rate and postoperative complications. The aim of the study is to investigate the clinical value of preoperative contrast-enhanced ultrasound (CEUS) for intraoperative SLNB in early breast cancer patients. Methods: A total of 201 patients scheduled for SLNB from September 2018 to April 2021 were collected consecutively. Preoperative CEUS was used to identify sentinel lymph nodes (SLN) and lymphatic drainage in breast cancer patients. Results: The SLN identification rate of CEUS was 93.0% (187/201) and four lymphatic drainage patterns were found: single LC to single SLN (70.0%), multiple LCs to single SLN (8.0%), single LC to multiple SLNs (10.2%), and multiple LCs to multiple SLNs (11.8%). The Sen, Spe, PPV, NPV, AUC of CEUS, US and CEUS + US in diagnosis of SLNs were 82.7%, 80.4%, 73.8%, 87.4%, 0.815; 70.7%, 77.7%, 68.0%, 79.8%, 0.742; and 86.7%, 77.7%, 72.2%, 89.7%, 0.822, respectively. There was no statistically significant difference between the diagnostic performance of CEUS and CEUS + US (p = 0.630). Conclusions: CEUS can be used to preoperatively assess the lymphatic drainage patterns and the status of the SLNs in early breast cancer to assist precision intraoperative SLNB.

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Section: Conventional Ultrasoundmentioning

confidence: 99%

Contrast-Enhanced Ultrasound for Precise Sentinel Lymph Node Biopsy in Women with Early Breast Cancer: A Preliminary Study

et al. 2021

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“…Coronado-Gutiérrez et al developed quantitative ultrasound image analysis techniques using deep learning to noninvasively diagnose axillary lymph node involvement in breast cancer using 118 lymph node ultrasound images. The achieved accuracy of this method was 86.4%, and its sensitivity and specificity were 84.9 and 87.7%, respectively [37].…”

Section: Image Classificationmentioning

confidence: 86%

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

et al. 2020

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Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

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“…Their proposed deep learning radiomics of ultrasonography model using both images of the primary tumor and ALN assigned 51% of the clinically over-treated patients to a low-risk group which could theoretically avoid LN biopsy. 19 A quantitative ultrasound image analysis using deep learning and machine learning studied by Cornado-Gutierrez et al 18 achieved accuracy of 86.4% and outperformed visual sonographic inspection by expert radiologists in diagnosing metastatic involvement of ALNs in breast cancer. Similarly, Drukker et al 33 proposed an automated quantitative ultrasound image analysis based on morphology and texture of ALNs.…”

Section: Discussionmentioning

confidence: 99%

“…15 Studies focused particularly on breast cancer metastasis prediction showed promising results for use as a second reader and assist in decision making. [16][17][18][19][20][21][22][23] However, this work to date has lacked standardization and relies on supervised machine learning models that require both high computational costs and extremely large datasets for algorithms training.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence

Tahmasebi

Sevrukov

et al. 2021

Ultrason Imaging

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The purpose of this study was to evaluate an artificial intelligence (AI) system for the classification of axillary lymph nodes on ultrasound compared to radiologists. Ultrasound images of 317 axillary lymph nodes from patients referred for ultrasound guided fine needle aspiration or core needle biopsy and corresponding pathology findings were collected. Lymph nodes were classified into benign and malignant groups with histopathological result serving as the reference. Google Cloud AutoML Vision (Mountain View, CA) was used for AI image classification. Three experienced radiologists also classified the images and gave a level of suspicion score (1–5). To test the accuracy of AI, an external testing dataset of 64 images from 64 independent patients was evaluated by three AI models and the three readers. The diagnostic performance of AI and the humans were then quantified using receiver operating characteristics curves. In the complete set of 317 images, AutoML achieved a sensitivity of 77.1%, positive predictive value (PPV) of 77.1%, and an area under the precision recall curve of 0.78, while the three radiologists showed a sensitivity of 87.8% ± 8.5%, specificity of 50.3% ± 16.4%, PPV of 61.1% ± 5.4%, negative predictive value (NPV) of 84.1% ± 6.6%, and accuracy of 67.7% ± 5.7%. In the three external independent test sets, AI and human readers achieved sensitivity of 74.0% ± 0.14% versus 89.9% ± 0.06% ( p = .25), specificity of 64.4% ± 0.11% versus 50.1 ± 0.20% ( p = .22), PPV of 68.3% ± 0.04% versus 65.4 ± 0.07% ( p = .50), NPV of 72.6% ± 0.11% versus 82.1% ± 0.08% ( p = .33), and accuracy of 69.5% ± 0.06% versus 70.1% ± 0.07% ( p = .90), respectively. These preliminary results indicate AI has comparable performance to trained radiologists and could be used to predict the presence of metastasis in ultrasound images of axillary lymph nodes.

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Quantitative Ultrasound Image Analysis of Axillary Lymph Nodes to Diagnose Metastatic Involvement in Breast Cancer

Cited by 26 publications

References 18 publications

Contrast-Enhanced Ultrasound for Precise Sentinel Lymph Node Biopsy in Women with Early Breast Cancer: A Preliminary Study

Contrast-Enhanced Ultrasound for Precise Sentinel Lymph Node Biopsy in Women with Early Breast Cancer: A Preliminary Study

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence

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