文章：

胶质母细胞瘤随访MRI深度学习模型的时间点特异性基准测试

Timepoint-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI

原文发布日期：22 December 2025

DOI: 10.3390/cancers18010036

类型: Article

开放获取: 是

英文摘要：

Background: Differentiating true tumor progression (TP) from treatment-related pseudoprogression (PsP) in glioblastoma remains challenging, especially at early follow-up. Methods: We present the first timepoint-specific, cross-sectional benchmarking of deep learning models for follow-up MRI using the Burdenko GBM Progression cohort (n= 180). We analyze different post-RT scans independently to test whether architecture performance depends on timepoint. Eleven representative DL families (CNNs, LSTMs, hybrids, transformers, and selective state-space models) were trained under a unified, QC-driven pipeline with patient-level cross-validation. Across both timepoints, accuracies were comparable (~0.70–0.74), but discrimination improved at the second follow-up, with F1 and AUC increasing for several models, indicating richer separability later in the care pathway. Results: A Mamba+CNN hybrid consistently offered the best accuracy–efficiency trade-off, while transformer variants delivered competitive AUCs at substantially higher computational cost, and lightweight CNNs were efficient but less reliable. Performance also showed sensitivity to batch size, underscoring the need for standardized training protocols. Notably, absolute discrimination remained modest overall, reflecting the intrinsic difficulty of TP vs. PsP and the dataset’s size and imbalance. Conclusions: These results establish a timepoint-aware benchmark and motivate future work incorporating longitudinal modeling, multi-sequence MRI, and larger multi-center cohorts.

摘要翻译： 

背景：在胶质母细胞瘤中，区分真正的肿瘤进展（TP）与治疗相关的假性进展（PsP）仍然具有挑战性，尤其是在早期随访阶段。方法：我们利用Burdenko GBM进展队列（n=180），首次针对随访MRI的深度学习模型进行了时间点特异性、横断面的基准测试。我们独立分析放疗后的不同扫描，以测试模型架构性能是否依赖于时间点。在统一、质量控制驱动的流程下，采用患者层面的交叉验证，对11个具有代表性的深度学习家族（包括CNN、LSTM、混合模型、Transformer及选择性状态空间模型）进行了训练。在两个时间点上，模型的准确率相近（约0.70–0.74），但在第二次随访时，模型的判别能力有所提升，多个模型的F1分数和AUC值增加，表明在治疗路径后期具有更丰富的可分离性。结果：Mamba+CNN混合模型始终提供最佳的准确率与效率平衡，而Transformer变体以显著更高的计算成本实现了有竞争力的AUC值，轻量级CNN模型效率高但可靠性较低。模型性能还对批次大小表现出敏感性，这强调了标准化训练协议的必要性。值得注意的是，模型的绝对判别能力总体上仍处于中等水平，这反映了区分TP与PsP的内在困难以及数据集的规模和类别不平衡问题。结论：这些结果建立了一个具有时间点意识的基准，并激励未来研究纳入纵向建模、多序列MRI以及更大规模的多中心队列。

原文链接：

Timepoint-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI