Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography

Zhao, Zhijin; Hou, Size; Li, Shuang; Sheng, Danli; Liu, Qi; Chang, Cai; Chen, Jiangang; Li, Jiawei

doi:10.1016/j.ultrasmedbio.2022.06.019

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…Many studies have been reported regarding the detection and classification of lesions in breast US images using AI models. 6 , 25 , 26 , 27 , 28 , 29 Our study has several strengths. First, prior research has primarily focused on differentiating between benign and malignant breast lesions, hence evaluating AI systems only on the images which contain either benign or malignant lesions.…”

Section: Discussionmentioning

confidence: 99%

“…First, prior research has primarily focused on differentiating between benign and malignant breast lesions, hence evaluating AI systems only on the images which contain either benign or malignant lesions. 6 , 27 , 28 , 29 In this work, we aim to develop a DL model to identify high-risk lesions in US images with benign findings, which is beneficial to early confirmation with immediate biopsy. Thus, it can further improve the detection rate of early breast cancer.…”

Section: Discussionmentioning

confidence: 99%

“…Second, most studies used only training and validation sets from one institution without an independent external test set. 25 , 28 , 29 In order to eliminate the differences in the disease spectrum in different centres, two external test sets were included for our model validation. Third, unlike other studies that only included B-mode US images, 27 , 29 our EDL-BC contains two separate feature extraction modules designed for B-mode and color Doppler US images.…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study

Liao

Gui

et al. 2023

eClinicalMedicine

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study

Liao

Gui

et al. 2023

eClinicalMedicine

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“…Therefore, the US + S-detect is of high clinical value for the diagnosis of breast cancer by both senior and junior radiologists. The malignancy rate of breast masses classified as BI-RADS 4a is approximately 2-10%, and most category 4a breast masses, although benign, may be subject to unnecessary biopsy (40,41). A previous meta-analysis by Park et al (42) reported that downgrading of BI-RADS category 4a breast masses to BI-RADS category 3 when US elastography was combined with US for diagnosis reduced unnecessary biopsies in 41.1% of breast category 4a nodules.…”

Section: Discussionmentioning

confidence: 99%

The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis

Chen¹,

Tong²,

Lin³

et al. 2022

Gland Surg

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Background: S-detect is an emerging computer-aided diagnosis (CAD) technique that provides a reference for radiologists to identify breast cancer. Some studies have shown that US (ultrasound) + S-detect can improve the diagnostic accuracy of junior radiologists more than senior radiologists, but the results are inconsistent in various studies. Therefore, this meta-analysis aimed to assess the value of S-detect combined with the US outcomes from senior and junior radiologists for the diagnosis of breast cancer. Methods: We searched the PubMed, Cochrane Library, Embase, Web of Science, and Wanfang databases, China Biology Medicine disc, China National Knowledge Infrastructure (CNKI), and VIP database for trials on the diagnostic accuracy of US + S-detect for the diagnosis of breast masses. The search time frame was from the date of establishment of the database to August 20, 2022. Two researchers independently screened the literature, extracted the information, and evaluated the quality of the included literature using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) scale. StataSE 15.1 software was utilized to assess pooled metrics, including sensitivity, specificity, and the area under the curve (AUC).Results: A total of 19 articles with 3,349 patients and 3,895 breast masses were included in this metaanalysis. Of these, seventeen articles evaluated the diagnostic performance of senior radiologists' US + S-detect for breast cancer, while twelve articles reported junior radiologists' diagnostic performance. The risk of bias was primarily attributed to patient selection, flow and timing. In the senior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.93 [95% confidence interval (CI): 0.89-0.95] and 0.86 (95% CI: 0.80-0.90), respectively, with an AUC of 0.96. As for the junior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.89 (95% CI: 0.83-0.93) and 0.79 (95% CI: 0.72-0.84), respectively, and the AUC was 0.91. Conclusions:The results of this meta-analysis showed that the pooled sensitivity and the AUC of both the senior and junior radiologist groups were high, with good diagnostic efficacy and high clinical application.However, the results of this study are highly heterogeneous and need to be validated by collecting more high-quality studies and accumulating a larger sample size.

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“…However, AI-assisted differential diagnosis is desirable for lesions with atypical US features. The authors' group previously evaluated the value of DL models in reducing the malignancy rate among breast imaging reporting and data system (BI-RADS) 4A lesions to achieve more accurate risk stratification (Zhao et al 2022). A further study is being conducted to evaluate the value of AI for diagnosing malignant breast tumors with atypical sonographic features using a large dataset from multiple centers.…”

Section: Differential Diagnosismentioning

confidence: 99%

Artificial intelligence in breast imaging: potentials and challenges

Li,

Sheng,

Chen

et al. 2023

Phys. Med. Biol.

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Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

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Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography

Cited by 9 publications

References 29 publications

Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study

Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study

The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis

Artificial intelligence in breast imaging: potentials and challenges

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