Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.
Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.
复制标题
基于机器学习的超声弹性成像乳腺肿瘤分类：一项系统的范围评估。
DOI：
10.3390/cancers14020367
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发表时间：
2022-01-12
期刊：
影响因子：
5.2
通讯作者：
Cheung JC
中科院分区：
文献类型：
--
作者：
Mao YJ;Lim HJ;Ni M;Yan WH;Wong DW;Cheung JC
乳腺癌是全球女性最常见的癌症之一。早期准确的乳腺肿瘤筛查可以提高生存率。超声弹性成像是一种非侵入性、非电离成像方法，用于表征乳腺癌筛查的病变，而机器学习技术可以提高计算机辅助诊断的准确性和可靠性。本综述重点关注机器学习模型在乳腺肿瘤分类中的最新发展和应用。超声弹性成像可以量化组织病变的硬度分布，并补充了用于乳腺癌筛查的传统 B 型超声。近年来，计算机辅助诊断的发展提高了系统的可靠性，而深度学习等机器学习的兴起，通过促进自动分割和肿瘤分类进一步扩展了其能力。本综述的目的是总结机器学习模型在超声弹性成像系统中用于乳腺肿瘤分类的应用。评论数据库包括 PubMed、Web of Science、CINAHL 和 EMBASE。十三 (n = 13) 篇文章符合审查资格。六篇文章对剪切波弹性成像进行了研究，而七项研究则侧重于应变弹性成像（其中 5 篇是徒手研究，2 篇是声辐射力研究）。传统的计算机视觉工作流程在应变弹性成像中很常见，使用不同的基于算法的方法、神经网络或支持向量机 (SVM) 来分离图像分割、特征提取和分类器功能。剪切波弹性成像通常采用集成功能任务的深度学习模型——卷积神经网络（CNN）。所有审查的文章都达到了≥80%的敏感性，而只有一半达到了可接受的特异性≥95%。深度学习模型不一定比传统计算机视觉工作流程表现得更好。然而，报告和计算方面存在不一致和不足，例如测试数据集、交叉验证以及避免过度拟合的方法。大多数研究没有报告损失或超参数。未来的研究可能会考虑使用带有注意力层的深度网络来自动定位目标对象，并进行在线训练以促进对顺序数据的有效重新训练。
Breast cancer is one of the most common cancers among women globally. Early and accurate screening of breast tumours can improve survival. Ultrasound elastography is a non-invasive and non-ionizing imaging approach to characterize lesions for breast cancer screening, while machine learning techniques could improve the accuracy and reliability of computer-aided diagnosis. This review focuses on the state-of-the-art development and application of the machine learning model in breast tumour classification. Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ≥80%, while only half of them attained acceptable specificity ≥95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.
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影响因子：
4.5
作者：
Chicco D
通讯作者：
Chicco D
DOI：
10.6061/clinics/2017(04)09
发表时间：
2017-04
期刊：
Clinics (Sao Paulo, Brazil)
影响因子：
--
作者：
da Costa Vieira RA;Biller G;Uemura G;Ruiz CA;Curado MP
通讯作者：
Curado MP
DOI：
10.1186/bcr2787
发表时间：
2010
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Evans A;Whelehan P;Thomson K;McLean D;Brauer K;Purdie C;Jordan L;Baker L;Thompson A
通讯作者：
Thompson A
影响因子：
2.3
作者：
Fujioka, Tomoyuki;Katsuta, Leona;Tateishi, Ukihide
通讯作者：
Tateishi, Ukihide
影响因子：
10.9
作者：
Gee, AH;Housden, RJ;Prager, RW
通讯作者：
Prager, RW