人工智能介导的超声影像组学在三阴性乳腺癌诊断和治疗中的研究进展与未来挑战
AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.
摘要(中文译文)
三阴性乳腺癌（TNBC）是一种侵袭性强、治疗选择有限、预后较差的乳腺癌亚型，其早期准确诊断和个体化治疗是临床面临的重大挑战。随着人工智能（AI）技术的快速发展，AI介导的超声影像组学为TNBC的无创诊断和治疗提供了新思路。该技术融合机器学习（ML）、深度学习（DL）和影像组学方法，实现对超声图像定量特征的高通量提取，并构建能够表征肿瘤异质性的预测模型。目前，AI驱动的超声影像组学在TNBC诊断方面已从基础鉴别诊断发展到多亚型分类，其诊断性能通过多模态图像融合得到进一步提升。在预后评估方面，模型通过整合瘤内和瘤周纹理特征、临床病理指标及其他相关因素，有效预测患者的无病生存期（DFS）和总生存期（OS）。然而，该技术向常规临床实践的转化面临多重挑战：标准化数据协议不足、模型可解释性有限以及缺乏严格的多中心验证研究。未来，应优先开展不同设备和医疗中心间标准化影像组学工作流程的建立研究，以及具有良好可解释性的高性能AI模型构建研究，并开展多中心前瞻性临床研究以验证其临床价值。
Abstract
Triple-negative breast cancer (TNBC) is a subtype of breast cancer with strong invasiveness, limited treatment options, and poor prognosis. Its early accurate diagnosis and individualized treatment are major challenges faced by the clinic. With the rapid development of artificial intelligence (AI) technology, AI-mediated ultrasonic radiomics provides new ideas for non-invasive diagnosis and treatment of TNBC. This technology integrates machine learning (ML), deep learning (DL), and radiomics methods to achieve high-throughput extraction of quantitative features from ultrasonic images and construct a predictive model capable of characterizing tumor heterogeneity. Currently, AI-driven ultrasonic radiomics for TNBC diagnosis has evolved from basic differential diagnosis to multi-subtype classification, with its diagnostic performance further improved via multimodal image fusion. For prognostic assessment, the models effectively predict patients' disease-free survival (DFS) and overall survival (OS) by integrating intratumoral and peritumoral texture features, clinicopathological indicators, and other relevant factors. Nevertheless, the translation of this technology into routine clinical practice faces multiple challenges: insufficient standardized data protocols, limited model interpretability, and lack of rigorous multicenter validation studies. In the future, research on the establishment of a standardized radiomics workflow among different devices and medical centers should be given priority as well as the research on the construction of high-performance AI models with good interpretability, and multicenter prospective clinical studies should be carried out to verify its clinical value.
如何引用
AMA
Zhihe Wang, Yifan Wang, Tao Yu, Yan Yi, Ke Xue, Wei Xu. AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1802259.
APA
Zhihe Wang, Yifan Wang, Tao Yu, Yan Yi, Ke Xue, Wei Xu (2026). AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1802259
GB/T 7714
Zhihe Wang, Yifan Wang, Tao Yu, Yan Yi, Ke Xue, Wei Xu. AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1802259.
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