发表机构
University of Central Florida（中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合三维基础编码器与时间动态网络的纵向框架，融合系列DCE-MRI与临床数据，在982例乳腺癌患者上实现了较好的pCR预测性能。
AI 中文摘要
病理完全缓解（pCR）是乳腺癌新辅助化疗（NAC）的重要终点，治疗过程中通过影像学预测pCR可支持治疗反应评估。现有多数基于影像学的方法依赖单一静态时间点，无法捕捉治疗期间发生的变化。本研究提出一种纵向框架，将冻结的三维基础编码器（Pillar-0）与时间动态网络（TDN）相结合，用于从预处理至术前四个临床时间点采集的系列DCE-MRI预测治疗反应。TDN将时间感知的体积嵌入与临床及治疗数据相结合以预测pCR。在来自I-SPY2与ACRIN-6698联合队列的982例患者上评估，当纵向三维成像与临床数据融合时，所提模型在所有报告指标上均表现出色（测试AUROC：73.6%，平衡准确率：69.1%）。临床变量提供最强的个体预测信号，但纵向三维成像与临床数据融合时可提供互补信息，提升pCR预测性能。本研究的源代码可在此URL获取。
英文摘要
Pathologic complete response (pCR) is an important endpoint in neoadjuvant chemotherapy (NAC) for breast cancer, and predicting pCR from imaging during treatment could support treatment response assessment. Many existing imaging-based approaches rely on a single static timepoint, which fails to capture changes that occur during treatment. In this work, we present a longitudinal framework that combines a frozen 3D foundation encoder (Pillar-0) with our Temporal Dynamics Network (TDN) to predict treatment response from serial Dynamic Contrast-Enhanced (DCE) MRI acquired across four clinical timepoints from pre-treatment to pre-surgery. The TDN combines time-aware volumetric embeddings with clinical and treatment data to predict pCR. Evaluated on 982 patients from the combined I-SPY2 and ACRIN-6698 cohort, the proposed model achieves strong performance across all reported metrics when longitudinal 3D imaging is fused with clinical data (test AUROC: 73.6%, balanced accuracy: 69.1%). While clinical variables provide the strongest individual predictive signal, longitudinal 3D imaging contributes complementary information when fused with clinical data, improving pCR prediction. Our source code is available at: https://github.com/omarftt/longitudinal_temporal_pillar.
CommentsAccepted at the Applications of Medical AI (AMAI) Workshop at MICCAI 2026
