(1) Background: Neoadjuvant chemotherapy (NAC) is an integral part of breast cancer management, and response to NAC is an important prognostic factor associated with improved survival outcomes. However, the current standard for response assessment relies on post-surgical histopathological analysis, which limits early therapeutic decision-making and treatment personalization. This study aimed to develop and evaluate a machine learning model that integrates pre-treatment MRI radiomics and clinical features to predict response to NAC in breast cancer patients. (2) Methods: In this study, a machine learning model was developed to predict breast cancer response to NAC using pre-treatment magnetic resonance imaging (MRI) radiomics and clinical data. Radiomic features were extracted from contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2) MRI sequences using both intratumoral and peritumoral segmentations. Furthermore, this study uniquely examined two response assessment criteria: (1) pathologic complete response (pCR) versus non-pCR, and (2) clinical response versus non-response. A total of 254 patients with biopsy-confirmed breast cancer who completed NAC were included. Radiomic features (n= 400) and clinical features (n= 7) were analyzed to build a predictive model employing the XGBoost classifier. Performance was measured in terms of accuracy, precision, sensitivity, specificity, F1-score, and AUC. (3) Results: The integration of radiomic features with clinical data significantly enhanced the predictive performance. For pCR and non-pCR prediction, the combined features model achieved an accuracy of 80% and AUC of 0.85, outperforming both the clinical features model (Accuracy = 68%, AUC = 0.81) and radiomic features model (Accuracy = 66%, AUC = 0.60). Similarly, for the clinical response and non-response prediction, the combined features model achieved an Accuracy of 74% and AUC of 0.75, outperforming both the clinical features model (Accuracy = 63%, AUC = 0.68) and radiomic features model (Accuracy = 66%, AUC = 0.57). (4) Conclusions: These findings highlight the synergistic effect of integrating clinical data and MRI-based radiomics to improve pre-treatment NAC response prediction, which has the potential to enable more precise and personalized treatment strategies.
（1）背景：新辅助化疗（NAC）是乳腺癌综合治疗的重要组成部分，其对NAC的反应是与改善生存结局相关的重要预后因素。然而，目前反应评估的金标准依赖于术后组织病理学分析，这限制了早期治疗决策和治疗个性化。本研究旨在开发和评估一种整合治疗前MRI影像组学特征与临床特征的机器学习模型，以预测乳腺癌患者对NAC的治疗反应。（2）方法：本研究开发了一种机器学习模型，利用治疗前磁共振成像（MRI）影像组学特征和临床数据预测乳腺癌对NAC的反应。影像组学特征从增强T1加权（CE-T1）和T2加权（T2）MRI序列中提取，并采用了瘤内和瘤周分割方法。此外，本研究创新性地考察了两种反应评估标准：（1）病理完全缓解（pCR）与非pCR；（2）临床缓解与无缓解。研究共纳入254例经活检确诊并完成NAC的乳腺癌患者。通过分析影像组学特征（n=400）和临床特征（n=7），采用XGBoost分类器构建预测模型。模型性能通过准确率、精确率、灵敏度、特异度、F1分数和AUC进行评估。（3）结果：影像组学特征与临床数据的整合显著提升了预测性能。在pCR与非pCR预测中，联合特征模型的准确率达到80%，AUC为0.85，优于临床特征模型（准确率=68%，AUC=0.81）和影像组学特征模型（准确率=66%，AUC=0.60）。同样，在临床缓解与无缓解预测中，联合特征模型的准确率达到74%，AUC为0.75，优于临床特征模型（准确率=63%，AUC=0.68）和影像组学特征模型（准确率=66%，AUC=0.57）。（4）结论：这些发现凸显了整合临床数据与基于MRI的影像组学在改善治疗前NAC反应预测方面的协同效应，有望为实现更精准和个性化的治疗策略提供支持。
肿瘤(癌症)患者之家