Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis

doi:10.21037/qims-24-679

LZUFE-KMS > 长青学院

	Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis
	Wang, Jinli 1; Tong, Jin 1; Li, Jun1 ; Cao, Chunli 1; Wang, Sirui 1; Bi, Tianyu2 ; Zhu, Peishan 1; Shi, Linan 1; Deng, Yaqian 1; Ma, Ting1
	2024-10
发表期刊	QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
卷号	14 期号:10 页码:7111-7127
摘要	Background: Breast cancer is one of the most common malignancies in women worldwide, and early and accurate diagnosis is crucial for improving treatment outcomes. Conventional ultrasound (CUS) is a widely used screening method for breast cancer; however, the subjective nature of interpreting the results can lead to diagnostic errors. The current study sought to estimate the effectiveness of using a GoogLeNet deep- learning convolutional neural network (CNN) model to identify benign and malignant breast masses based on CUS. Methods: A literature search was conducted of the Embase, PubMed, Web of Science, Wanfang, China National Knowledge Infrastructure (CNKI), and other databases to retrieve studies related to GoogLeNet deep-learning CUS-based models published before July 15, 2023. The diagnostic performance of the GoogLeNet models was evaluated using several metrics, including pooled sensitivity (PSEN), pooled specificity (PSPE), the positive likelihood ratio (PLR), the negative likelihood ratio (NLR), the diagnostic odds ratio (DOR), and the area under the curve (AUC). The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies Scale (QUADAS). The eligibility of the included literature were independently searched and assessed by two authors. Results: All of the 12 studies that used pathological findings as the gold standard were included in the meta-analysis. The overall average estimation of sensitivity and specificity was 0.85 [95% confidence interval (CI): 0.80-0.89] and 0.86 (95% CI: 0.78-0.92), respectively. The PLR and NLR were 6.2 (95% CI: 3.9-9.9) and 0.17 (95% CI: 0.12-0.23), respectively. The DOR was 37.06 (95% CI: 20.78-66.10). The AUC was 0.92 (95% CI: 0.89-0.94). No obvious publication bias was detected. Conclusions: The GoogLeNet deep-learning model, which uses a CNN, achieved good diagnostic results in distinguishing between benign and malignant breast masses in CUS-based images.
关键词	GoogLeNet deep learning meta-analysis breast mass ultrasound (US)
DOI	10.21037/qims-24-679
收录类别	SCIE
ISSN	2223-4292
语种	英语
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001354315400007
出版者	AME PUBLISHING COMPANY
原始文献类型	Article
EISSN	2223-4306
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/38514
专题	长青学院工商管理学院国际经济与贸易学院
通讯作者	Li, Jun; Cui, Xinwu
作者单位	1.Shihezi Univ, Affiliated Hosp 1, Med Coll, Dept Ultrasound, 107 North 2nd Rd, Shihezi 832008, Peoples R China; 2.Lanzhou Univ Finance & Econ, Sch Business Adm, Lanzhou, Peoples R China; 3.Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Med Ultrasound, 288 Xintian Ave, Wuhan 430101, Peoples R China
推荐引用方式 GB/T 7714	Wang, Jinli,Tong, Jin,Li, Jun,et al. Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis[J]. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,2024,14(10):7111-7127.
APA	Wang, Jinli.,Tong, Jin.,Li, Jun.,Cao, Chunli.,Wang, Sirui.,...&Cui, Xinwu.(2024).Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,14(10),7111-7127.
MLA	Wang, Jinli,et al."Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis".QUANTITATIVE IMAGING IN MEDICINE AND SURGERY 14.10(2024):7111-7127.