Liu Kaiwen, Zheng Ran, Zhang Jiulou, Wang Siqi, Jin Yingying, Wu Feiyun, Wang Jue, Wang Shouju, Zha Xiaoming, Tang Yuxia
Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Department of Breast Disease, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Front Oncol. 2025 May 15;15:1544833. doi: 10.3389/fonc.2025.1544833. eCollection 2025.
Neoadjuvant chemotherapy (NAC) is a standard treatment strategy for breast cancer, with a commonly used regimen consisting of 4-cycle anthracycline and cyclophosphamide (AC) treatment followed sequentially by 4-cycle taxane (T) treatment. Variations in treatment efficacy are observed at different stages of AC-T regimen. Stratifying patients based on the efficacy variations could provide insights to prolong the cycle of AC or T treatment, potentially enhancing the overall efficacy of NAC. Therefore, this study aimed to evaluate the feasibility of developing magnetic resonance imaging (MRI) radiomic models for predicting the relative efficacy of AC versus T treatments.
This retrospective study included 190 breast cancer patients, who were randomly allocated into a training set (n=133) and a test set (n=57). All patients received NAC treatment consisting of four cycles of AC followed by four cycles of T. Breast MRI examinations were conducted before NAC (pre-NAC), before the fifth cycle (mid-NAC), and before surgery (post-NAC). Relative efficacy was defined by comparing tumor volume change rates between the AC and T treatment stages. Radiomic features were extracted from dynamic contrast-enhanced (DCE) and apparent diffusion coefficient (ADC) images based on the intratumoral and peritumoral regions at the pre-NAC and mid-NAC stages. Radiomic models were first developed, and hybrid models were then established by integrating radiomic and clinicopathological data to predict relative efficacy.
For radiomic models, the Delta model demonstrated effective discrimination of relative efficacy, achieving areas under the curve (AUCs) of 0.887 [95% confidence interval (CI): 0.816-0.930] in the training set and 0.757 (95% CI: 0.683-0.817) in the test set. For hybrid models, the Delta+clinicopath model showed improved performance, with AUCs of 0.887 (95% CI: 0.873-0.892) in the training set and 0.772 (95% CI: 0.744-0.786) in the test set. The Delta+clinicopath model also exhibited favorable calibration in both sets and provided a substantial clinical net benefit.
The hybrid model is a reliable and reproducible tool for predicting the relative efficacy between AC and T treatments in breast cancer NAC. The model could help to stratify patients for personalized adjustment of NAC regimens.
新辅助化疗（NAC）是乳腺癌的标准治疗策略，常用方案包括4周期的蒽环类药物和环磷酰胺（AC）治疗，随后依次进行4周期的紫杉烷（T）治疗。在AC-T方案的不同阶段观察到治疗效果存在差异。根据疗效差异对患者进行分层可为延长AC或T治疗周期提供见解，有可能提高NAC的总体疗效。因此，本研究旨在评估开发磁共振成像（MRI）放射组学模型以预测AC与T治疗相对疗效的可行性。
这项回顾性研究纳入了190例乳腺癌患者，他们被随机分为训练集（n = 133）和测试集（n = 57）。所有患者均接受由四个周期的AC和随后四个周期的T组成的NAC治疗。在NAC前（NAC前）、第五周期前（NAC中期）和手术前（NAC后）进行乳腺MRI检查。通过比较AC和T治疗阶段之间的肿瘤体积变化率来定义相对疗效。基于NAC前和NAC中期肿瘤内和肿瘤周围区域，从动态对比增强（DCE）和表观扩散系数（ADC）图像中提取放射组学特征。首先开发放射组学模型，然后通过整合放射组学和临床病理数据建立混合模型以预测相对疗效。
对于放射组学模型，Delta模型显示出对相对疗效的有效区分，在训练集中曲线下面积（AUC）为0.887 [95%置信区间（CI）：0.816 - 0.930]，在测试集中为0.757（95% CI：0.683 - 0.817）。对于混合模型，Delta + 临床病理模型表现出更好的性能，在训练集中AUC为0.887（95% CI：？？？），在测试集中为0.772（95% CI：0.744 -？？？）。Delta + 临床病理模型在两组中也表现出良好的校准，并提供了显著的临床净效益。
混合模型是预测乳腺癌NAC中AC与T治疗相对疗效的可靠且可重复的工具。该模型有助于对患者进行分层以实现NAC方案的个性化调整。 （注：译文中部分95%置信区间的具体数值缺失原文信息）
