LoGo-MR：通过高效多切片建模筛查乳腺MRI以预测癌症风险
LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling
AI总结：
本文提出LoGo-MR框架，通过2.5D局部-全局结构建模实现乳腺癌五年风险预测，结合邻居切片编码和transformer增强的多实例学习，提升预测性能和可解释性。
摘要：
高效的乳腺癌风险预测对于大规模人群筛查至关重要。乳腺MRI提供功能性信息用于个性化风险评估。然而，有效建模仍具挑战性，因为完全3D CNNs在高计算成本下捕捉体积上下文，而轻量级2D CNNs无法建模切片间连续性。重要的是，乳腺MRI建模用于短期和长期乳腺癌风险分层仍处于探索阶段。在本研究中，我们提出LoGo-MR，一种用于五年乳腺癌风险预测的2.5D局部-全局结构建模框架。与临床解释一致，我们的框架首先采用邻切片编码捕捉与短期风险相关的细微局部线索。然后整合transformer增强的多实例学习（MIL）来建模分布的全局模式，与长期风险相关并提供可解释的切片重要性。我们进一步将此框架应用于轴向、矢状和冠状平面作为LoGo3-MR，以捕捉互补的体积信息。这种多平面公式使voxel级风险显著性映射成为可能，可能帮助放射科医生在乳腺MRI解读中定位风险相关区域。在大规模乳腺MRI筛查队列（约7500例）上评估，我们的方法优于2D/3D基线和现有SOTA MIL方法，实现了1-至5年预测的AUCs为0.77-0.69，并在3D CNNs上提高了C-index约6%。LoGo3-MR进一步通过三个平面的可解释定位提升整体性能，且在七个backbone上的验证显示一致的提升。这些结果突显了基于高效MRI的乳腺癌风险分层在大规模筛查中的临床潜力。代码将公开发布。
英文摘要：
Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional information for personalized risk assessment. Yet effective modeling remains challenging as fully 3D CNNs capture volumetric context at high computational cost, whereas lightweight 2D CNNs fail to model inter-slice continuity. Importantly, breast MRI modeling for shor- and long-term BC risk stratification remains underexplored. In this study, we propose LoGo-MR, a 2.5D local-global structural modeling framework for five-year BC risk prediction. Aligned with clinical interpretation, our framework first employs neighbor-slice encoding to capture subtle local cues linked to short-term risk. It then integrates transformer-enhanced multiple-instance learning (MIL) to model distributed global patterns related to long-term risk and provide interpretable slice importance. We further apply this framework across axial, sagittal, and coronal planes as LoGo3-MR to capture complementary volumetric information. This multi-plane formulation enables voxel-level risk saliency mapping, which may assist radiologists in localizing risk-relevant regions during breast MRI interpretation. Evaluated on a large breast MRI screening cohort (~7.5K), our method outperforms 2D/3D baselines and existing SOTA MIL methods, achieving AUCs of 0.77-0.69 for 1- to 5-year prediction and improving C-index by ~6% over 3D CNNs. LoGo3-MR further improves overall performance with interpretable localization across three planes, and validation across seven backbones shows consistent gains. These results highlight the clinical potential of efficient MRI-based BC risk stratification for large-scale screening. Code will be released publicly.
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