Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
乳腺癌在全球范围内仍是女性中最常见的恶性肿瘤。为每位患者审慎选择适宜的治疗方案对有效管理乳腺癌至关重要。新辅助化疗（NACT）在该疾病的综合治疗中发挥着关键作用。通过术前实施化疗，NACT成为缩小肿瘤体积的有力手段，可能使手术创伤更小，甚至使原本无法手术的肿瘤获得手术机会。然而，不同患者对NACT的反应存在显著差异，这构成了重大挑战。为应对这一挑战，研究者致力于开发能够识别NACT获益人群与非获益人群的预测模型。此类模型有望降低治疗成本，并推动乳腺癌管理向更高效、精准的方向发展。因此，本文综述旨在实现两个目标：一是确定与NACT疗效相关的最有效影像组学标志物；二是探讨将影像学图像提取的影像组学标志物与病理学标志物整合，能否提升NACT疗效预测的准确性。本综述将深入探讨这些研究问题，并展望利用人工智能技术预测NACT疗效这一新兴研究方向，从而勾勒乳腺癌治疗的未来图景。
肿瘤(癌症)患者之家