Background:Response to neoadjuvant chemotherapy (NAC) is a key prognostic indicator in breast cancer, yet current assessment relies on postoperative pathology. This study investigated the use of deep features derived from pre-treatment MRI and CT scans, in conjunction with clinical variables, to predict treatment response a priori.Methods:Two response endpoints were analyzed: pathologic complete response (pCR) versus non-pCR, and responders versus non-responders, with response defined as a reduction in tumor size of at least 30%. Intratumoral and peritumoral segmentations were generated on contrast-enhanced T1-weighted (CE-T1) and T2-weighted MRI, as well as contrast-enhanced CT images of tumors. Deep features were extracted from these regions using ResNet10, ResNet18, ResNet34, and ResNet50 architectures pre-trained with MedicalNet. Handcrafted radiomic features were also extracted for comparison. Feature selection was conducted with minimum redundancy maximum relevance (mRMR) followed by recursive feature elimination (RFE), and classification was performed using XGBoost across ten independent data partitions.Results:A total of 177 patients were analyzed in this study. ResNet34-derived features achieved the highest overall classification performance under both criteria, outperforming handcrafted features and deep features from other ResNet architectures. For distinguishing pCR from non-pCR, ResNet34 achieved a balanced accuracy of 81.6%, whereas handcrafted radiomics achieved 77.9%. For distinguishing responders from non-responders, ResNet34 achieved a balanced accuracy of 73.5%, compared with 70.2% for handcrafted radiomics.Conclusions:Deep features extracted from routinely acquired MRI and CT, when combined with clinical information, improve the prediction of NAC response in breast cancer. This multimodal framework demonstrates the value of deep learning-based approaches as a complement to handcrafted radiomics and provides a basis for more individualized treatment strategies.
背景：新辅助化疗（NAC）反应是乳腺癌的关键预后指标，但目前评估依赖于术后病理。本研究探讨利用治疗前MRI与CT扫描提取的深度特征，结合临床变量，在治疗前预测治疗反应。 方法：分析两个反应终点：病理完全缓解（pCR）与非pCR，以及应答者与非应答者（应答定义为肿瘤缩小至少30%）。在增强T1加权（CE-T1）、T2加权MRI及肿瘤增强CT图像上分别进行瘤内和瘤周分割。使用经MedicalNet预训练的ResNet10、ResNet18、ResNet34和ResNet50架构从这些区域提取深度特征，同时提取手工放射组学特征作为对比。特征选择采用最小冗余最大相关（mRMR）结合递归特征消除（RFE），并基于XGBoost在十个独立数据分区进行分类。 结果：本研究共纳入177例患者。在两种分类标准下，ResNet34提取的深度特征均取得最佳总体分类性能，优于手工特征及其他ResNet架构的深度特征。在区分pCR与非pCR时，ResNet34的平衡准确率达81.6%，而手工放射组学为77.9%；在区分应答者与非应答者时，ResNet34的平衡准确率为73.5%，手工放射组学为70.2%。 结论：从常规MRI与CT中提取的深度特征结合临床信息，可提升乳腺癌NAC反应的预测效能。该多模态框架证明了基于深度学习的方法可作为手工放射组学的有效补充，并为更个体化的治疗策略提供了依据。
肿瘤(癌症)患者之家