通过建模患者与多模态数据关系，提升癌症生存预测准确率和缺失数据鲁棒性。
Bipartite Patient-Modality Graph Learning with Event-Conditional Modelling of Censoring for Cancer Survival Prediction
- 构建双部图学习患者与多模态数据关系，增强对缺失模态的适应能力。
- 在5个公开数据集上平均C-index提升3.1%，显著优于现有最优方法。
- 可插拔的删失建模模块使8个基线模型平均性能提升1.3%，适合临床预测场景。
准确预测癌症患者生存期对个性化治疗至关重要。然而，现有研究仅关注已知生存风险样本间的关系，未充分利用删失样本的价值，且在模态缺失场景下性能易下降，甚至影响推理。本文提出一种双部患者-模态图学习与事件条件删失建模的生存预测方法（CenSurv）。首先，利用图结构建模多模态数据并提取表示；其次，设计双部图模拟不同模态缺失情形下的患者-模态关系，采用完整-不完整对齐策略挖掘模态无关特征；最后，设计可插拔的事件条件删失建模（ECMC）模块，通过动态动量累积置信度筛选可靠删失数据，为其分配更准确的生存时间，并作为非删失数据参与训练。在5个公开癌症数据集上的综合评估表明，CenSurv相比最优现有方法平均C-index提升3.1%，在多种模态缺失场景下仍具优异鲁棒性。此外，引入ECMC模块后，8个基线模型在5个数据集上平均C-index提升1.3%。代码已开源：https://github.com/yuehailin/CenSurv。
原文摘要 · Abstract (English)
Accurately predicting the survival of cancer patients is crucial for personalized treatment. However, existing studies focus solely on the relationships between samples with known survival risks, without fully leveraging the value of censored samples. Furthermore, these studies may suffer performance degradation in modality-missing scenarios and even struggle during the inference process. In this study, we propose a bipartite patient-modality graph learning with event-conditional modelling of censoring for cancer survival prediction (CenSurv). Specifically, we first use graph structure to model multimodal data and obtain representation. Then, to alleviate performance degradation in modality-missing scenarios, we design a bipartite graph to simulate the patient-modality relationship in various modality-missing scenarios and leverage a complete-incomplete alignment strategy to explore modality-agnostic features. Finally, we design a plug-and-play event-conditional modeling of censoring (ECMC) that selects reliable censored data using dynamic momentum accumulation confidences, assigns more accurate survival times to these censored data, and incorporates them as uncensored data into training. Comprehensive evaluations on 5 publicly cancer datasets showcase the superiority of CenSurv over the best state-of-the-art by 3.1% in terms of the mean C-index, while also exhibiting excellent robustness under various modality-missing scenarios. In addition, using the plug-and-play ECMC module, the mean C-index of 8 baselines increased by 1.3% across 5 datasets. Code of CenSurv is available at https://github.com/yuehailin/CenSurv.
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