发表机构
The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
STRIDE框架通过结合病灶先验引导的空间表示和时间条件潜在转移，利用成对MRI及间隔预测胶质母细胞瘤的稳定、假性进展或真性进展，在Burdenko任务上取得高AUC和F1分数。
AI 中文摘要
胶质母细胞瘤（GBM）是一种侵袭性的原发性脑肿瘤，治疗后常规通过纵向MRI进行监测。区分稳定疾病（SD）、假性进展（PsP）和真性进展（TP）仍然具有挑战性，因为这些状态尽管具有不同的时间轨迹，但可能表现出重叠的MRI表现。现有的纵向方法在建模扫描特定的空间变异性、可变的随访间隔以及观察到的随访状态及其纵向变化的互补信息方面仍面临挑战。我们提出STRIDE，一个用于区间条件疾病演变的时空表示框架，该框架将成对的治疗后MRI扫描及其扫描间间隔作为输入，预测SD、PsP或TP。病灶先验引导的空间表示结合了SoftGate和自适应窗口层次Transformer（AWHT），以强调病灶相关区域同时保留周围上下文。时间条件潜在转移使用配对级别上下文和实际扫描间间隔来估计访问之间依赖于间隔的表示变化。观察-转移融合将转移估计的随访表示与直接观察到的随访表示集成，以共同表征随访状态及其纵向变化。使用BraTS2024开发和评估病灶先验生成器，而在LUMIERE上的纵向预训练支持在Burdenko下游适应之前的迁移。在Burdenko三分类任务上，STRIDE实现了0.816的宏ROC-AUC和0.796的宏F1分数。这些结果支持其在更可靠的纵向治疗后GBM状态评估中的潜力。
英文摘要
Glioblastoma (GBM), an aggressive primary brain tumor, is routinely monitored with longitudinal MRI after treatment. Distinguishing stable disease (SD), pseudoprogression (PsP), and true progression (TP) remains challenging because these states can show overlapping MRI appearances despite different temporal trajectories. Existing longitudinal methods still face challenges in modeling scan-specific spatial variability, variable follow-up intervals, and complementary information from the observed follow-up state and its longitudinal change. We propose STRIDE, a framework for spatial-temporal representation of interval-conditioned disease evolution that takes paired post-treatment MRI scans and their inter-scan interval as input and predicts SD, PsP, or TP. The lesion-prior-guided spatial representation combines SoftGate and an Adaptive-window Hierarchical Transformer (AWHT) to emphasize lesion-related regions while preserving surrounding context. The time-conditioned latent transition uses pair-level context and the actual inter-scan interval to estimate interval-dependent representation changes between visits. The observed--transition fusion integrates the transition-estimated follow-up representation with the directly observed follow-up representation to jointly characterize the follow-up state and its longitudinal change. BraTS2024 is used to develop and evaluate the lesion-prior generator, while longitudinal pretraining on LUMIERE supports transfer before downstream adaptation to Burdenko. On the Burdenko three-class task, STRIDE achieves a macro ROC--AUC of 0.816 and a macro F1-score of 0.796. These results support its potential for more reliable longitudinal post-treatment GBM state assessment.
Comments37 pages, 11 figures
