使用治疗前弥散和对比增强磁共振成像及临床变量预测新辅助化疗反应

中文摘要 AI 辅助

在新辅助化疗前预测病理完全缓解可能有助于为乳腺癌患者制定更具针对性的治疗方案。本研究提出了一种仅使用治疗前数据的深度学习模型，结合表观弥散系数图、动态对比增强磁共振成像和临床变量。研究使用了公开的ACRIN 6698/I-SPY2多中心数据集。该架构采用预训练的EfficientNet-B0编码器进行图像特征提取，并与临床信息进行晚期融合。评估了多个临床变量，包括年龄、种族、组织学类型、HR/HER2亚型、SBR分级和最大直径。仅HR/HER2亚型提高了受试者工作特征曲线下面积（AUC）的平均值，并保留在最终模型中。使用分层五折交叉验证，单独的表观弥散系数图实现了0.79的平均AUC，而动态对比增强磁共振成像实现了0.74。加入HR/HER2亚型后，性能分别提升至0.83和0.81。最终配置使用两种成像模态和HR/HER2亚型，实现了0.86的AUC。这些结果支持治疗前多模态学习用于反应预测，但在临床使用前仍需外部验证。

英文摘要

Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer patients. This work proposes a deep-learning model for pretreatment data only, combining apparent diffusion coefficient maps, dynamic contrast-enhanced magnetic resonance imaging, and clinical variables. The study uses the public ACRIN 6698/I-SPY2 multicenter dataset. The architecture employs EfficientNet-B0 pretrained encoders for image feature extraction and late fusion with clinical information. Multiple clinical variables were evaluated, including age, race, histological type, HR/HER2 subtype, SBR grade, and maximum diameter. Only HR/HER2 subtype improved the average area under the receiver operating characteristic curve (AUC) and was retained in the final model. Using stratified five-fold cross-validation, standalone apparent diffusion coefficient maps achieved a mean AUC of 0.79, whereas dynamic contrast-enhanced magnetic resonance imaging achieved 0.74. Adding HR/HER2 subtype improved performance to 0.83 and 0.81, respectively. The final configuration, using both imaging modalities and HR/HER2 subtype, achieved an AUC of 0.86. These results support pretreatment multimodal learning for response prediction, although external validation is required before clinical use.

发表机构

• University of Oviedo（奥维耶多大学）
• University of A Coruña（拉科鲁尼亚大学）
• The University of Texas at Austin（德克萨斯大学奥斯汀分校）
• Purdue University（普渡大学）
• Asturias Central University Hospital (HUCA)（阿斯图里亚斯中央大学医院）
• Cabueñes University Hospital (CAHU)（卡布埃涅斯大学医院）

机构由 AI 辅助整理，请以论文原文为准。