Bhalla Kanika, Xiao Qi, Luna José Marcio, Podany Emily, Ahmad Tabassum, Ademuyiwa Foluso O, Davis Andrew, Bennett Debbie Lee, Gastounioti Aimilia
Breast Image Computing Lab, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States.
Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States.
BJR Artif Intell. 2024 Nov 13;1(1):ubae016. doi: 10.1093/bjrai/ubae016. eCollection 2024 Jan.
Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10%-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Towards this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumour visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumours that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics, and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive, and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions, and prognostic evaluations for TNBC.
乳腺癌是女性中最常见且致命的癌症之一。三阴性乳腺癌（TNBC）约占乳腺癌诊断病例的10%-15%，是一种侵袭性分子乳腺癌亚型，在其诊断、治疗和预后方面面临重大挑战。这迫切需要为TNBC开发更有效、更个性化的影像生物标志物。朝着这个方向，用于放射成像的人工智能（AI）发挥着重要作用，它利用乳腺放射图像的独特优势，常规用于TNBC的诊断、分期和治疗规划，并提供高分辨率的全肿瘤可视化，同时结合了AI的巨大潜力，以阐明人眼可能不易察觉的肿瘤的解剖和功能特性。在这篇综述中，我们综合了AI在辅助TNBC诊断、治疗和预后方面的当前先进放射成像应用。我们的目标是全面概述基于影像组学和深度学习的AI发展及其在过去十年（2013-2024年）对推进TNBC管理的影响。为使综述完整，我们首先简要介绍AI、影像组学和深度学习。接下来，我们重点关注在TNBC中评估的基于AI的乳腺放射图像临床相关诊断、预测和预后模型。我们最后讨论AI在推进TNBC诊断、治疗反应预测和预后评估方面的机遇和未来方向。
