使用 GoogLeNet 深度学习模型根据传统超声波区分良性和恶性乳腺肿块:系统回顾和荟萃分析。

IF 2.9 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Quantitative Imaging in Medicine and Surgery Pub Date : 2024-10-01 Epub Date: 2024-09-26 DOI:10.21037/qims-24-679
Jinli Wang, Jin Tong, Jun Li, Chunli Cao, Sirui Wang, Tianyu Bi, Peishan Zhu, Linan Shi, Yaqian Deng, Ting Ma, Jixue Hou, Xinwu Cui
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引用次数: 0

摘要

背景:乳腺癌是全球妇女最常见的恶性肿瘤之一,早期准确诊断对提高治疗效果至关重要。传统超声(CUS)是一种广泛使用的乳腺癌筛查方法;然而,解读结果的主观性可能会导致诊断错误。本研究试图评估使用 GoogLeNet 深度学习卷积神经网络 (CNN) 模型根据 CUS 识别良性和恶性乳腺肿块的有效性:对Embase、PubMed、Web of Science、万方、中国国家知识基础设施(CNKI)等数据库进行文献检索,检索2023年7月15日之前发表的基于GoogLeNet深度学习CUS模型的相关研究。GoogLeNet 模型的诊断性能采用多个指标进行评估,包括集合灵敏度 (PSEN)、集合特异度 (PSPE)、正似然比 (PLR)、负似然比 (NLR)、诊断几率比 (DOR) 和曲线下面积 (AUC)。纳入研究的质量采用诊断准确性研究质量评估量表(QUADAS)进行评估。纳入文献的资格由两位作者独立检索和评估:所有 12 项以病理结果为金标准的研究都纳入了荟萃分析。敏感性和特异性的总体平均估计值分别为 0.85 [95% 置信区间 (CI):0.80-0.89] 和 0.86 (95% CI:0.78-0.92)。PLR和NLR分别为6.2(95% CI:3.9-9.9)和0.17(95% CI:0.12-0.23)。DOR为37.06(95% CI:20.78-66.10)。AUC为0.92(95% CI:0.89-0.94)。未发现明显的发表偏倚:使用 CNN 的 GoogLeNet 深度学习模型在区分基于 CUS 图像的良性和恶性乳腺肿块方面取得了良好的诊断效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.

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.

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来源期刊
Quantitative Imaging in Medicine and Surgery
Quantitative Imaging in Medicine and Surgery Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.20
自引率
17.90%
发文量
252
期刊介绍: Information not localized
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