Wang Lijia, Wang Yongchen, Yang Li, Ren Jialiang, Xu Qian, Zhai Yingmin, Zhou Tao
Department of Medical Imaging, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Department of Breast Cancer Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Front Oncol. 2025 Jul 30;15:1539644. doi: 10.3389/fonc.2025.1539644. eCollection 2025.
This study aimed to develop and validate a predictive model for tumor shrinkage patterns in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer patients undergoing neoadjuvant chemotherapy (NAC).
A retrospective analysis was conducted on 227 HR+/HER2- breast cancer patients with a desire for breast conservation, examining their clinicopathological characteristics, traditional MRI features, and radiomics features. Patients were divided into training and validation cohorts in a 7:3 ratio. Tumor shrinkage patterns were classified into Type I and Type II based on RECIST 1.1 criteria. A clinical model was established using Ki67 quantification and enhancement pattern. Radiomics features were extracted and analyzed using machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). A combined clinical-radiomics model was also developed.
The clinical model achieved an area under the curve (AUC) of 0.624 in the training cohort and 0.551 in the validation cohort. The RF radiomics model showed the highest predictive performance with an AUC of 0.826 in the training cohort and 0.808 in the validation cohort. The combined clinical-radiomics model further improved prediction accuracy, with an AUC of 0.831 in the training cohort and 0.810 in the validation cohort.
Radiomics features based on baseline MRI significantly enhance the prediction of tumor shrinkage patterns in HR+/HER2- breast cancer patients. This approach aids in the early identification of patients likely to benefit from breast-conserving surgery and facilitates timely treatment adjustments.
本研究旨在开发并验证一种预测模型，用于预测接受新辅助化疗（NAC）的激素受体阳性、人表皮生长因子受体2阴性（HR+/HER2-）乳腺癌患者的肿瘤缩小模式。
对227例有保乳意愿的HR+/HER2-乳腺癌患者进行回顾性分析，研究其临床病理特征、传统MRI特征和放射组学特征。患者按7:3的比例分为训练组和验证组。根据RECIST 1.1标准，将肿瘤缩小模式分为I型和II型。使用Ki67定量和强化模式建立临床模型。采用逻辑回归（LR）、支持向量机（SVM）、决策树（DT）和随机森林（RF）等机器学习算法提取并分析放射组学特征。还开发了一种临床-放射组学联合模型。
临床模型在训练组中的曲线下面积（AUC）为0.624，在验证组中为0.551。RF放射组学模型的预测性能最高，训练组的AUC为0.826，验证组为0.808。临床-放射组学联合模型进一步提高了预测准确性，训练组的AUC为0.831，验证组为0.810。
基于基线MRI的放射组学特征显著提高了HR+/HER2-乳腺癌患者肿瘤缩小模式的预测能力。这种方法有助于早期识别可能从保乳手术中获益的患者，并便于及时调整治疗方案。
