From the Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY (R.L.G., J.H., S.E.-W., J.T., K.P.); Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands (J.T.); AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands (J.T.); Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru (J.H.); and Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY (S.T.).
Invest Radiol. 2024 Mar 1;59(3):230-242. doi: 10.1097/RLI.0000000000001010.
Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
新辅助治疗（PST）是局部晚期乳腺癌患者的首选治疗方法，目前也常用于早期乳腺癌患者。尽管影像学检查仍然是准确评估 PST 反应的关键，但使用影像学预测 PST 反应不仅具有更好的预后价值，还可以避免潜在的毒性治疗，减少不良影响，加速新的靶向治疗的实施，并减轻选定患者的手术延迟。为了应对放射科医生通过对磁共振成像（MRI）上肿瘤进行定性、主观评估来预测 PST 反应的能力有限，人工智能增强 MRI 与经典机器学习，以及最近的深度学习一起，已经被用于预测反应，无论是在 PST 开始之前还是在治疗的早期阶段。这篇综述提供了对人工智能在评估和预测 PST 反应的 MRI 中的当前应用的概述，并讨论了其临床实施的挑战和局限性。
