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探索乳腺癌成像的演变:传统与新兴模式综述

Exploring the Evolution of Breast Cancer Imaging: A Review of Conventional and Emerging Modalities.

作者信息

Cerdas Maria Gabriela, Farhat Jana, Elshafie Sara I, Mariyam Faina, James Lina, Qureshi Arifa K, Potru Monica, Paliwei Paerhati, Joshi Megha R, Abraham Godwin, Siddiqui Humza F

机构信息

Medicine, Universidad de Ciencias Médicas (UCIMED), San Jose, CRI.

Diagnostic Radiology, Faculty of Medicine, Lebanese University, Beirut, LBN.

出版信息

Cureus. 2025 Apr 22;17(4):e82762. doi: 10.7759/cureus.82762. eCollection 2025 Apr.

DOI:10.7759/cureus.82762
PMID:40416096
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12098770/
Abstract

Breast cancer (BC) is one of the leading causes of malignancy among women, and its prevalence is exponentially rising globally. Early and accurate imaging is critical for early detection, diagnosis, and treatment planning. This comprehensive review explores the current status of BC imaging, from the conventional methods such as mammography, ultrasound (US) and magnetic resonance imaging (MRI) to more advanced techniques including contrast-enhanced imaging, tomosynthesis, and molecular breast imaging (MBI). Conventional imaging remains the foundation for screening, as mammography is the most widely preferred modality. US and MRI are usually employed in dense breasts in highly suspicious cases that are not detected on a mammogram. However, the limitations posed by these traditional techniques can be curtailed using advanced modalities to enhance diagnostic accuracy. These emerging techniques provide faster and earlier detection of malignancy, particularly in high-risk patients, and substantially reduce the burden of missed cases. Emerging technologies, including photoacoustic imaging (PAI) and contrast-enhanced ultrasound (CEUS), show promising potential in visualizing microvascular structures and enhancing diagnostic accuracy. Additionally, artificial intelligence (AI) is revolutionizing BC imaging across all modalities by optimizing interpretation, enhancing sensitivity, and enabling personalized risk assessment. Although technological innovation continues to improve imaging quality and diagnostic precision, challenges such as cost, accessibility, overdiagnosis, and disparities in care remain a concern. Moving forward, a collaborative multimodal strategy that incorporates personalized imaging protocols and equitable access will be crucial for improving BC screening and management. The future of breast imaging lies not in replacing existing modalities but in developing a system where each technology complements the other, leading to earlier detection, more effective treatment, and enhanced outcomes.

摘要

乳腺癌(BC)是女性恶性肿瘤的主要病因之一,其在全球的患病率呈指数级上升。早期准确的成像对于早期检测、诊断和治疗规划至关重要。本综述探讨了BC成像的现状,从传统方法如乳腺X线摄影、超声(US)和磁共振成像(MRI)到更先进的技术,包括对比增强成像、断层合成和分子乳腺成像(MBI)。传统成像仍然是筛查的基础,因为乳腺X线摄影是最广泛使用的检查方式。US和MRI通常用于乳腺致密且乳腺X线摄影未检测出的高度可疑病例。然而,这些传统技术的局限性可以通过先进的检查方式来减少,以提高诊断准确性。这些新兴技术能够更快、更早地检测出恶性肿瘤,尤其是在高危患者中,并大幅减少漏诊病例的负担。新兴技术,包括光声成像(PAI)和对比增强超声(CEUS),在可视化微血管结构和提高诊断准确性方面显示出有前景的潜力。此外,人工智能(AI)正在通过优化解读、提高敏感性和实现个性化风险评估,彻底改变所有检查方式的BC成像。尽管技术创新不断提高成像质量和诊断精度,但成本、可及性、过度诊断和护理差异等挑战仍然令人担忧。展望未来,结合个性化成像方案和公平可及性的协作多模态策略对于改善BC筛查和管理至关重要。乳腺成像的未来不在于取代现有检查方式,而在于开发一种系统,其中每种技术相互补充,从而实现更早的检测、更有效的治疗和更好的治疗效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12d5/12098770/ba10b153d431/cureus-0017-00000082762-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12d5/12098770/ba10b153d431/cureus-0017-00000082762-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12d5/12098770/ba10b153d431/cureus-0017-00000082762-i01.jpg

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Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.探讨人工智能对乳腺癌管理个性化影响的系统研究。
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