Chinese association of ultrasound in medicine and engineering, superficial organs and peripheral vessels committee expert consensus on clinical frequently asked questions in breast ultrasonography, June 2018

Jiang, Yuxin; Chen, Wen; Luo, Baoming; Peng, Yulan; Wang, Zhili; Xu, Jinfeng; Zhou, Jinhua; Zhou, Qi; Bao, Liang; Chen, Li; Cc, Lin; Chen, Qin; Cong, Shan; Cui, Kefei; Cui, Xin-Wu; Fang, Qinmao; Li, Fengsheng; Li, Huiwen; Li, Jing; Li, Jianchu; Li, Junlai; Li, Quanshui; Li, Tianliang; Li, Tao; Li, Yanjiang; Li, Zhengyi; Liu, Jia; Liu, Yong; Luo, Jun; Ma, Buyun; Ma, Zhe; Nie, Fang; Peng, Chengzhong; Sui, Xianwei; Sun, Hongyan; Wang, Hongqiao; Wang, Jing; Wang, Yan; Wu, Changjun; Xiao, Ying; Xiong, Hongyan; Xu, Dong; Xue, Ensheng; Xue, Lifang; Yan, Song-li; Yang, Lichun; Yong, Qiang; Zhan, Weiwei; Zhang, Sheng; Xue-zhen, Zhang; Zhang, Jianxing; Zhou, Xian‐Li; Lin, Xi; Yin, Shanyu; Zhao, Qiyu

doi:10.4103/jcrt.jcrt_509_18

Cited by 4 publications

(6 citation statements)

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“…7 Department of Ultrasonography, Henan Provincial People's Hospital, Zhengzhou, China. 8 Department of Ultrasound, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China. 9 Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.…”

Section: Discussionmentioning

confidence: 99%

“…US has become an indispensable tool for the screening and diagnosis of breast cancer due to its safety, lower cost, and ease of accessibility in China [8]. With the introduction of the Breast Imaging Reporting and Data System (BI-RADS) for US, the terminology and criteria for describing and classifying breast lesions have become standardized with good diagnostic performance.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

Lin

et al. 2022

Insights Imaging

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Background Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model’s ability to assist the radiologists. Methods A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model’s ability to assist the radiologists using two different methods. Results The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities. Conclusions The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

Lin

et al. 2022

Insights Imaging

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“…Because the breasts of most Chinese women are relatively small and dense, mammography was easy to miss breast tumors ( 3 ). Because of its convenience, no radiation effects, low cost, and rapidity, ultrasonography has become the preferred method for screening breast cancer in China ( 4 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the accuracy of ultrasound elastography and BI-RADS classification of breast masses located within the superficial fat layer of the glands

Xue¹,

Zhang²

2022

Gland Surg

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Background: The surrounding tissue of lesions located in the mammary fat layer is mainly fat, not mammary glands. Are the currently used Breast Imaging Reporting and Data System (BI-RADS) classifications and ultrasound elasticity appropriate for such lesions? In the present study, we discuss the value of elastography and BI-RADS classification in the evaluation of masses in the superficial fat layer of the breast.Methods: Seventy-five breast masses within the fat layer that met inclusion criteria were included in the study. Using histopathology as the gold standard, we retrospectively analyzed whether the ultrasound elastography and BI-RADS classification results were consistent with the pathological results.Results: Histological analysis showed that 73 tumors were benign and 2 were malignant. According to the BI-RADS classification and treatment principle, 60% (45/75) of the masses were classified into category 4 and require breast biopsy. But only 4.4% (2/45) of these masses were malignant, and 95.6% (43/45) were overtreated. If we consider the masses with well-defined margins and within the fat layer on the surface of the breast glands as likely benign (BI-RADS category 3), the probability of malignancy is 1.4%. This is consistent with the BI-RADS classification probability of malignancy. According to this BI-RADS classification, only 1.3% (1/75) of patients required biopsy. Conclusions:The findings of this study suggest that breast masses located in the fat layer are prone to be classified into category 4 by BI-RADS and thus be subjected to unnecessary biopsies. Ultrasound elastography can easily misdiagnose benign masses as malignant. It is suggested that ultrasound elastography can downgrade the BI-RADS classification, but not upgrade it. It is more reasonable for these breast masses to be classified as BI-RADS 3 for follow-up observation when the boundary is clear.

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“…7 Ultrasonography has been widely accepted as a powerful examination for breast cancer assessment because of its convenience, absence of radiation, low cost, and good performance in dense breasts. [8][9][10] Ultrasound elastography (UE) is an emerging tool that can provide tissue stiffness information as measurable physical quantities for differentiating benign from malignant breast lesions-malignant lesions with high stiffness, while benign lesions with low stiffness. [11][12][13][14][15][16] The World Federation of Ultrasound in Medicine and Biology (WFUMB) and the European Federation of Ultrasound in Medicine and Biology guidelines and recommendations for the clinical practice of ultrasound elastography for breast demonstrated the combination of strain elastography (SE) and conventional ultrasound (US) could avoid unnecessary biopsy by downgrading the Breast Imaging Reporting and Data System (BI-RADS) classification.…”

mentioning

confidence: 99%

“…Ultrasonography has been widely accepted as a powerful examination for breast cancer assessment because of its convenience, absence of radiation, low cost, and good performance in dense breasts 8–10 . Ultrasound elastography (UE) is an emerging tool that can provide tissue stiffness information as measurable physical quantities for differentiating benign from malignant breast lesions—malignant lesions with high stiffness, while benign lesions with low stiffness 11–16 .…”

mentioning

confidence: 99%

Elastography Assisted BI‐RADS in the Preoperative Breast Magnetic Resonance Imaging 4a Lesions in China

Tang

Wang

Gong

et al. 2022

J of Ultrasound Medicine

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Objectives A considerable number of benign lesions, especially category 4a lesions on Breast Imaging Reporting and Data System Magnetic Resonance Imaging (BI‐RADS‐MRI), were biopsied according to BI‐RADS‐MRI, which was a diagnostic imaging challenge. This study aimed to evaluate the diagnostic performance of ultrasound elastography (UE) assisted Breast Imaging Reporting and Data System (BI‐RADS) for BI‐RADS‐MRI category 4a lesions. Methods Between January 2017 and December 2019, 228 breast lesions categorized as BI‐RADS‐MRI 4a were included. Conventional ultrasound (US) and UE were performed to evaluate each lesion. Pathology results were used as the gold standard. The diagnostic performances of different UE methods and our re‐assessment proposal were evaluated. Results When BI‐RADS‐MRI category 4a, BI‐RADS‐US category 3‐4a, the stiffness of soft or intermediate in elasticity assessment according to the fifth edition of the BI‐RADS atlas, strain ratio < 1.335, age ≤ 52 years, and the maximum diameter of lesion ≤20 mm were simultaneously met, an ultrasound‐guided empty needle biopsy was not recommended, but short‐term ultrasound follow‐up for 3–6 months was recommended, and biopsy was performed after changes in evaluation. In this way, 95 of 228 BI‐RADS‐MRI category 4a lesions avoided biopsies, and the number of patients with biopsies decreased by 41.7%. Conclusions UE offers benefits in the characterization of BI‐RADS‐MRI category 4a lesions. Ultrasound and elastography can help optimize therapy recommendations for BI‐RADS‐MRI category 4a lesions by our re‐assessment proposal.

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Chinese association of ultrasound in medicine and engineering, superficial organs and peripheral vessels committee expert consensus on clinical frequently asked questions in breast ultrasonography, June 2018

Cited by 4 publications

References 0 publications

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

Analysis of the accuracy of ultrasound elastography and BI-RADS classification of breast masses located within the superficial fat layer of the glands

Elastography Assisted BI‐RADS in the Preoperative Breast Magnetic Resonance Imaging 4a Lesions in China

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