用四次动态增强MRI预测乳腺癌新辅助化疗效果，提升预后判断准确率。
Longitudinal 3D Foundation Modeling for Neoadjuvant Breast Cancer Response Prediction from Serial DCE-MRI
- 融合时间序列3D影像与临床数据，构建时序动态预测模型。
- 在982例患者上实现73.6%的测试AUROC和69.1%的平衡准确率。
- 适合关注肿瘤影像动态变化与精准治疗评估的研究者。
病理完全缓解（pCR）是乳腺癌新辅助化疗的重要终点，通过治疗期间影像预测pCR可支持疗效评估。现有方法多依赖单一静态时间点，难以捕捉治疗过程中的变化。本文提出一种纵向框架，结合冻结的3D基础编码器（Pillar-0）与作者自研的时间动态网络（TDN），利用从治疗前至术前共四个时间点获取的序列动态对比增强（DCE）MRI数据进行治疗反应预测。TDN将时间感知的体素嵌入与临床及治疗数据融合，以预测pCR。在整合I-SPY2与ACRIN-6698队列的982名患者上评估，当纵向3D影像与临床数据融合时，模型在所有报告指标上表现优异（测试AUROC：73.6%，平衡准确率：69.1%）。尽管临床变量提供最强单独预测信号，但纵向3D影像在融合后贡献互补信息，进一步提升预测性能。源代码已公开于https://github.com/omarftt/longitudinal_temporal_pillar。
原文摘要 · Abstract (English)
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.
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