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人工智能在乳腺 MRI 中的应用:今天和明天。

AI Applications to Breast MRI: Today and Tomorrow.

机构信息

Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA.

AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.

出版信息

J Magn Reson Imaging. 2024 Dec;60(6):2290-2308. doi: 10.1002/jmri.29358. Epub 2024 Apr 5.

DOI:10.1002/jmri.29358
PMID:38581127
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11452568/
Abstract

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

摘要

在乳腺成像领域,对乳腺成像服务的需求持续增长,部分原因是乳腺诊断和治疗的影像学适应证不断扩大。由于提供这些服务的人力没有以相同的速度增长,因此人工智能(AI)在乳腺成像中的应用得到了显著的推动,以最大限度地提高工作流程效率和生产力,同时提高诊断准确性和患者的治疗效果。到目前为止,AI 在乳腺成像中的应用最为先进,包括乳腺 X 线摄影和数字乳腺断层合成技术,其次是超声,而由于 MRI 检查的复杂性和可用数据集较少,AI 在乳腺磁共振成像(MRI)中的应用进展并不快。尽管如此,人们对 AI 增强型乳腺 MRI 应用仍有着浓厚的兴趣,即使乳腺 MRI 的使用和适应证仍在不断扩大。本文综述了 AI 成像分析的基本概念,并随后回顾了 AI 增强 MRI 解读的应用案例,即乳腺 MRI 的分诊和病灶检测、病灶分类、治疗反应预测、风险评估和图像质量。最后,本文还展望了 AI 在乳腺 MRI 中应用的障碍和促进因素。证据水平:5 级 技术功效:第 6 级。

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J Magn Reson Imaging. 2024 Sep;60(3):1190-1200. doi: 10.1002/jmri.29139. Epub 2023 Nov 16.
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Reducing Gadolinium Contrast With Artificial Intelligence.利用人工智能减少钆造影剂用量。
J Magn Reson Imaging. 2024 Sep;60(3):848-859. doi: 10.1002/jmri.29095. Epub 2023 Oct 31.
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Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.
探索MRI和乳腺钼靶检查的病变特征以用于PTEN错构瘤肿瘤综合征中的乳腺癌检测
Cancers (Basel). 2025 Mar 2;17(5):856. doi: 10.3390/cancers17050856.
深度学习重建在高质量 T2 加权乳腺磁共振成像中的应用评估。
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A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.用于人工智能研究的公共可用 MRI 数据集调查。
J Magn Reson Imaging. 2024 Feb;59(2):450-480. doi: 10.1002/jmri.29101. Epub 2023 Oct 27.
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Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review.人工神经网络在乳腺影像诊断临床实践中应用的障碍和促进因素:范围综述。
Eur Radiol. 2024 Mar;34(3):2096-2109. doi: 10.1007/s00330-023-10181-6. Epub 2023 Sep 2.
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Radiol Imaging Cancer. 2023 Jul;5(4):e230009. doi: 10.1148/rycan.230009.
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Eur J Radiol. 2023 Sep;166:110948. doi: 10.1016/j.ejrad.2023.110948. Epub 2023 Jun 25.
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Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.深度学习在高 b 值扩散加权乳腺 MRI 中的图像质量评估。
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