4D-ACFNet：一种基于4D注意力机制的结直肠癌肝转移预后框架，融合多模态时空特征
4D-ACFNet: A 4D Attention Mechanism-Based Prognostic Framework for Colorectal Cancer Liver Metastasis Integrating Multimodal Spatiotemporal Features
AI总结：
本文提出4D-ACFNet框架，通过4D时空注意力机制、跨模态动态校准和个性化时间预测，解决结直肠癌肝转移术后预后预测中多模态融合与动态建模难题，在197例患者上实现100%时间邻接准确率。
摘要：
结直肠癌肝转移（CRLM）的术后预后预测仍然具有挑战性，原因在于肿瘤异质性、肝脏微环境的动态演变以及多模态数据融合不足。为解决这些问题，我们提出了4D-ACFNet，这是第一个在统一架构中协同整合轻量级时空建模、跨模态动态校准和个性化时间预测的框架。具体而言，该框架引入了一种新颖的4D时空注意力机制，采用时空可分离卷积（参数数量减少41%）和虚拟时间戳编码来建模术后动态过程（如肝脏再生和脂肪变性）的年际演变模式。对于跨模态特征对齐，集成了Transformer层以联合优化模态对齐损失和解耦损失，有效抑制了临床-影像数据中的尺度不匹配和冗余干扰。此外，我们设计了一个动态预后决策模块，通过时间上采样和门控分类头生成个性化的年际复发风险热图，克服了传统方法在时间动态建模和跨模态对齐方面的局限性。在197例CRLM患者上的实验表明，该模型实现了100%的时间邻接准确率（TAA），性能显著超越现有方法。本研究建立了CRLM术后动态监测的首个时空建模范式。所提出的框架可扩展至多癌种转移的预后分析，推动精准外科从“空间切除”迈向“时空治愈”。
英文摘要：
Postoperative prognostic prediction for colorectal cancer liver metastasis (CRLM) remains challenging due to tumor heterogeneity, dynamic evolution of the hepatic microenvironment, and insufficient multimodal data fusion. To address these issues, we propose 4D-ACFNet, the first framework that synergistically integrates lightweight spatiotemporal modeling, cross-modal dynamic calibration, and personalized temporal prediction within a unified architecture. Specifically, it incorporates a novel 4D spatiotemporal attention mechanism, which employs spatiotemporal separable convolution (reducing parameter count by 41%) and virtual timestamp encoding to model the interannual evolution patterns of postoperative dynamic processes, such as liver regeneration and steatosis. For cross-modal feature alignment, Transformer layers are integrated to jointly optimize modality alignment loss and disentanglement loss, effectively suppressing scale mismatch and redundant interference in clinical-imaging data. Additionally, we design a dynamic prognostic decision module that generates personalized interannual recurrence risk heatmaps through temporal upsampling and a gated classification head, overcoming the limitations of traditional methods in temporal dynamic modeling and cross-modal alignment. Experiments on 197 CRLM patients demonstrate that the model achieves 100% temporal adjacency accuracy (TAA), with performance significantly surpassing existing approaches. This study establishes the first spatiotemporal modeling paradigm for postoperative dynamic monitoring of CRLM. The proposed framework can be extended to prognostic analysis of multi-cancer metastases, advancing precision surgery from "spatial resection" to "spatiotemporal cure."
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
