(1) Background: Response to neoadjuvant chemotherapy (NAC) is a key prognostic indicator in breast cancer. However, current response evaluation methods rely on histopathological assessment after surgery, delaying opportunities for early treatment adaptation. This study aimed to develop a machine learning model by integrating radiomic features extracted from pre-treatment, contrast-enhanced computed tomography (CT) images with baseline clinical variables to predict NAC response before therapy initiation. (2) Methods: The study investigated two categories of response: (i) pathologic complete response (pCR) versus non-pCR, and (ii) clinical response versus non-response, where clinical response was defined as a reduction in tumor size of at least 30%, encompassing both complete and partial responses. Radiomic features (n= 214) were extracted from intratumoral and peritumoral regions of pre-treatment CT images. Clinical variables (n= 7) were also incorporated to enhance predictive capability. A predictive model was developed using XGBoost algorithm, and performance was evaluated across ten independent data partitions using metrics including accuracy, precision, sensitivity, specificity, F1-score, and AUC. (3) Results: A total of 177 patients were enrolled in the study. The combined clinical-radiomic model set exhibited superior predictive performance compared to models based solely on either radiomic or clinical features. For pCR classification, integrating clinical and radiomic features produced the strongest model, achieving 82.8% accuracy with an AUC of 0.846. The clinical model alone reached 71.4% accuracy and an AUC of 0.797, while the radiomic model achieved 67.5% accuracy and an AUC of 0.615. For clinical response classification, the combined model again outperformed the individual models, achieving 71.7% accuracy with an AUC of 0.725, compared with 65.0% accuracy and an AUC of 0.666 for the clinical model, and 65.6% accuracy with an AUC of 0.615 for the radiomic model. (4) Conclusions: These results demonstrate that integrating CT radiomic features with clinical information enhances the prediction of NAC response, supporting the potential for earlier and more personalized therapeutic decision-making in breast cancer management.
（1）背景：新辅助化疗（NAC）反应是乳腺癌的关键预后指标。然而，目前反应评估方法依赖于术后组织病理学评估，延迟了早期治疗调整的机会。本研究旨在通过整合治疗前增强CT图像提取的影像组学特征与基线临床变量，开发一种机器学习模型，以在治疗开始前预测NAC反应。（2）方法：本研究探讨了两类反应：（i）病理完全缓解（pCR）与非pCR；（ii）临床反应与非反应，其中临床反应定义为肿瘤大小减少至少30%，包括完全缓解和部分缓解。从治疗前CT图像的瘤内和瘤周区域提取了影像组学特征（n=214）。同时纳入临床变量（n=7）以增强预测能力。使用XGBoost算法开发预测模型，并通过准确率、精确率、灵敏度、特异度、F1分数和AUC等指标在十个独立数据分区上评估性能。（3）结果：本研究共纳入177例患者。与仅基于影像组学或临床特征的模型相比，临床-影像组学联合模型展现出更优的预测性能。在pCR分类中，整合临床与影像组学特征构建的模型性能最强，准确率达82.8%，AUC为0.846。单独临床模型的准确率为71.4%，AUC为0.797；而单独影像组学模型的准确率为67.5%，AUC为0.615。在临床反应分类中，联合模型同样优于单一模型，准确率达71.7%，AUC为0.725；而临床模型的准确率为65.0%，AUC为0.666；影像组学模型的准确率为65.6%，AUC为0.615。（4）结论：这些结果表明，整合CT影像组学特征与临床信息可增强NAC反应的预测能力，为乳腺癌管理中更早期、更个性化的治疗决策提供了潜在支持。
A Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using CT Radiomics
肿瘤(癌症)患者之家