Aim: This study investigates treatment response prediction to neoadjuvant chemotherapy (NACT) in breast cancer patients, using longitudinal contrast-enhanced magnetic resonance images (CE-MRI) and clinical data. The goal is to develop machine learning (ML) models to predict pathologic complete response (PCR binary classification) and 5-year relapse-free survival status (RFS binary classification). Method: The proposed framework includes tumour segmentation, image registration, feature extraction, and predictive modelling. Using the image registration method, MRI image features can be extracted and compared from the original tumour site at different time points, therefore monitoring the intratumor changes during NACT process. Four feature extractors, including one radiomics and three deep learning-based (MedicalNet, Segformer3D, SAM-Med3D) were implemented and compared. In combination with three feature selection methods and four ML models, predictive models are built and compared. Results: The proposed image registration-based feature extraction consistently improves the predictive models. In the PCR and RFS classification tasks logistic regression model trained on radiomic features performed the best with an AUC of 0.88 and classification accuracy of 0.85 for PCR classification, and AUC of 0.78 and classification accuracy of 0.72 for RFS classification. Conclusions: It is evidenced that the image registration method has significantly improved performance in longitudinal feature learning in predicting PCR and RFS. The radiomics feature extractor is more effective than the pre-trained deep learning feature extractors, with higher performance and better interpretability.

翻译：目的：本研究旨在利用纵向对比增强磁共振成像（CE-MRI）与临床数据，预测乳腺癌患者对新辅助化疗（NACT）的治疗反应。目标是开发机器学习（ML）模型，以预测病理完全缓解（PCR二分类）及5年无复发生存状态（RFS二分类）。方法：所提出的框架包括肿瘤分割、图像配准、特征提取和预测建模。通过图像配准方法，可从不同时间点的原始肿瘤部位提取并比较MRI图像特征，从而监测NACT过程中的瘤内变化。我们实现并比较了四种特征提取器，包括一种放射组学方法和三种基于深度学习的方法（MedicalNet、Segformer3D、SAM-Med3D）。结合三种特征选择方法和四种ML模型，构建并比较了预测模型。结果：基于图像配准的特征提取方法持续提升了预测模型的性能。在PCR和RFS分类任务中，基于放射组学特征训练的逻辑回归模型表现最佳：PCR分类的AUC为0.88，分类准确率为0.85；RFS分类的AUC为0.78，分类准确率为0.72。结论：研究证明，图像配准方法显著提升了纵向特征学习在预测PCR和RFS方面的性能。放射组学特征提取器相较于预训练的深度学习特征提取器更为有效，具有更高的预测性能和更好的可解释性。