用多平面2.5D模型高效预测乳腺癌五年风险，兼顾解释性与精度。
LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling
- 分层建模：局部捕捉短期风险，全局融合长期风险模式。
- 五年人群预测AUC达0.77-0.69，比3D CNN提升约6% C-index。
- 支持三维切片定位，帮助医生识别高风险区域，适合临床筛查使用。
高效的乳腺癌（BC）风险预测对大规模人群筛查至关重要。乳腺MRI提供功能信息，可用于个性化评估。然而，全3D卷积网络计算成本高，轻量2D网络又难以建模切片间连续性。尤其短/长期风险分层建模仍缺乏探索。本文提出LoGo-MR，一种用于五年内乳腺癌风险预测的2.5D局部-全局结构建模框架。该框架首先通过邻近切片编码捕捉与短期风险相关的微弱局部特征；再结合增强型多实例学习（MIL）Transformer，建模与长期风险相关的分布式全局模式，并输出可解释的切片重要性。进一步构建多平面版本LoGo3-MR，融合轴向、矢状面和冠状面信息，实现体素级风险显著性映射，辅助放射科医生定位风险区域。在超7500例乳腺MRI筛查队列上评估，本方法优于2D/3D基线及现有最优MIL方法，1-5年预测的AUC为0.77-0.69，相较3D CNN提升约6% C-index。LoGo3-MR在三平面下表现更优且具可解释定位能力，七种主干网络验证均保持一致增益。结果表明，该方法具备大规模筛查的临床潜力。代码将公开。
原文摘要 · Abstract (English)
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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