用双注意力残差网络，从MRI图像准确预测乳腺癌HER2状态
Dual-attention ResNet outperforms transformers in HER2 prediction on DCE-MRI
- 设计双注意力残差网络，融合三阶段动态增强MRI数据
- 在1149例数据上达0.75准确率和0.74 AUC，优于变压器模型
- 无需微调即可跨机构验证，适合临床影像辅助诊断
乳腺癌是女性中最常见的癌症，HER2状态对治疗决策至关重要。通过动态对比增强MRI（DCE-MRI）非侵入式预测HER2状态可简化诊断流程，减少对活检的依赖。然而，将高动态范围的DCE-MRI预处理为适合预训练神经网络的8位RGB格式具有挑战性，且归一化策略显著影响模型性能。我们基于多中心队列（n=1,149，来自I-SPY试验）训练了一种三头双注意力残差网络，该网络处理三个DCE时相的RGB融合时间序列，并在BreastDCEDL_AMBL数据集（n=43个病灶）上进行外部验证。模型表现优于基于变压器的架构，在I-SPY测试数据上达到0.75准确率和0.74 AUC。N4偏场校正轻微降低了性能。未微调情况下，外部验证获得0.66 AUC，证明了跨机构泛化能力。结果表明，双注意力机制能有效捕捉可迁移的时空特征，推动乳腺癌影像中可重复的深度学习生物标志物发展。
原文摘要 · Abstract (English)
Breast cancer is the most diagnosed cancer in women, with HER2 status critically guiding treatment decisions. Noninvasive prediction of HER2 status from dynamic contrast-enhanced MRI (DCE-MRI) could streamline diagnostics and reduce reliance on biopsy. However, preprocessing high-dynamic-range DCE-MRI into standardized 8-bit RGB format for pretrained neural networks is nontrivial, and normalization strategy significantly affects model performance. We benchmarked intensity normalization strategies using a Triple-Head Dual-Attention ResNet that processes RGB-fused temporal sequences from three DCE phases. Trained on a multicenter cohort (n=1,149) from the I-SPY trials and externally validated on BreastDCEDL_AMBL (n=43 lesions), our model outperformed transformer-based architectures, achieving 0.75 accuracy and 0.74 AUC on I-SPY test data. N4 bias field correction slightly degraded performance. Without fine-tuning, external validation yielded 0.66 AUC, demonstrating cross-institutional generalizability. These findings highlight the effectiveness of dual-attention mechanisms in capturing transferable spatiotemporal features for HER2 stratification, advancing reproducible deep learning biomarkers in breast cancer imaging.
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