想理解脑肿瘤MRI评估的黑箱？TRACE用概念瓶颈让模型决策透明可验证，在LUMIERE上表现接近非可解释方法，值得看。
TRACE是一个概念瓶颈模型，用于对纵向3D MRI进行4分类胶质母细胞瘤反应评估，严格对齐RANO 2.0标准。该模型在LUMIERE数据集上通过5折患者交叉验证，实现了4类macro F1为0.4769，二分类（进展vs非进展）macro F1为0.7085。TRACE先预测肿瘤测量作为根概念，再通过确定性规则计算下游RANO衍生概念，并引入扫描间隔和新病灶信息。消融实验表明专家RANO图和干预一致性训练对性能至关重要，干预实验显示修正概念可提升下游预测。
TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment
Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as RANO. However, most deep learning methods predict response labels directly from imaging features, which limits clinical inspection, verification, and correction. We introduce TRACE, a RANO 2.0-aligned concept bottleneck model for interpretable 4-class glioblastoma response classification on longitudinal 3D MRI. TRACE processes paired baseline and follow-up multimodal MRI scans with a shared 3D vision encoder, predicts clinically meaningful tumor measurements as root concepts, computes downstream RANO-derived concepts through deterministic rules, and incorporates scan interval and new-lesion information as passthrough concepts. This design frames response assessment as structured concept reasoning rather than direct image-to-label prediction. Using 5-fold patient-wise cross-validation on the LUMIERE dataset, TRACE achieves a 4-class macro F1 of 0.4769 and a binary progression-versus-non-progression macro F1 of 0.7085. It improves over a concept bottleneck baseline and remains within the range of published non-interpretable deep learning approaches. Ablation studies show that the expert RANO graph and intervention-consistency training are important for performance, while intervention experiments demonstrate that correcting concepts can improve downstream predictions. These results suggest that structured concept bottlenecks offer a transparent and clinically aligned direction for longitudinal glioblastoma response assessment, while highlighting the need for larger protocol-aligned datasets and external validation.