收稿日期: 2025-12-01
网络出版日期: 2026-04-29
基金资助
国家自然科学基金项目(92059201)
Advances in the application of multimodal ultrasound and artificial intelligence in the early diagnosis and prognostic prediction of breast cancer
Received date: 2025-12-01
Online published: 2026-04-29
随着乳腺癌发病率的持续上升，早期诊断与准确预后评估成为提高生存率与优化个体化治疗的关键。多模态超声通过整合弹性成像、黏弹性成像、超声造影、微血流成像及光声/超声等技术，能够在无创、动态与实时的条件下提供关于病灶形态、力学性质、血流灌注和组织功能的多维信息，弥补传统灰阶超声在微血管、组织硬度及生物力学表征方面的不足。人工智能（AI）特别是深度学习方法在图像特征自动提取、多模态数据融合和预测建模方面显示出卓越能力，为超声影像从图像获取向临床决策支持的转化提供技术支撑。本文系统综述了近年来多模态超声技术进展及其与AI结合在乳腺癌早期诊断、良恶性鉴别、淋巴结转移预测及分子分型推断中的研究成果与局限；进一步探讨了AI在病灶检测、自动分割、影像组学特征融合及基于临床与影像多源数据的预后预测模型构建中的应用价值，并分析模型可解释性、数据标准化、跨中心泛化能力与临床可接受性等关键挑战，提出促进临床转化的策略建议。多模态超声与AI的协同发展有望将超声从辅助检测工具演进为乳腺癌早期诊疗与全程管理的重要组成部分，但其广泛应用仍需技术成熟、临床验证与政策支持的共同推动。
钟佳倩 , 谢晓燕 , 郑艳玲 . 多模态超声与人工智能在乳腺癌早期诊断与预后预测中的应用进展[J]. 实用医学杂志, 2026 , 42(9) : 1491 -1500 . DOI: 10.3969/j.issn.1006-5725.2026.09.002
With the rising incidence of breast cancer， early diagnosis and accurate prognostic evaluation have become pivotal for improving survival rates and optimizing personalized treatment. Multimodal ultrasound， by integrating technologies such as elastography， viscoelastic imaging， contrast-enhanced ultrasound， microflow imaging， and photoacoustic/ultrasound， can provide multi-dimensional information regarding lesion morphology， mechanical properties， blood perfusion， and tissue function in a non-invasive， dynamic， and real-time manner. This compensates for the limitations of conventional grayscale ultrasound in characterizing microvasculature， tissue stiffness， and biomechanical properties. Artificial Intelligence （AI）， particularly deep learning methods， has demonstrated remarkable capabilities in automatic feature extraction from images， multimodal data fusion， and predictive modeling， thereby providing technical support for transforming ultrasound imaging from image acquisition into clinical decision-making support. This paper systematically reviews recent advances in multimodal ultrasound technologies and their integration with AI in the areas of early breast cancer diagnosis， benign-malignant differentiation， prediction of lymph node metastasis， and prediction of molecular subtypes， summarizing both research achievements and existing limitations. Furthermore， it explores the application value of AI in lesion detection， automatic segmentation， radiomics feature fusion， and the construction of prognostic prediction models based on multi-source clinical and imaging data. Key challenges such as model interpretability， data standardization， cross-institution generalization capability， and clinical acceptability are analyzed， and strategic recommendations for promoting clinical translation are proposed. The synergistic development of multimodal ultrasound and AI holds promise for evolving ultrasound from an auxiliary detection tool into a pivotal component of early diagnosis， treatment， and comprehensive management throughout the breast cancer care continuum. However， its widespread application still requires concerted advancement in technological maturity， clinical validation， and policy support.
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〉 |