Abstract: Integrating pathological images with genomic data through deep learning can significantly improve the accuracy of cancer prognosis prediction. However, in clinical practice, only a subset of patients have complete genomic sequencing results, which limits the comprehensive application of multimodal models. How to fully leverage limited genomic data to enhance the prognostic capability of pathological models is crucial for improving the clinical applicability and generalization ability of multimodal approaches. To this end, this paper proposes VMEF, a pathology enhancement framework based on the Variational Mixture of Experts（VMoE） module, designed to address training scenarios where pathological images are complete but genomic data is partially missing. The framework learns cross-modal mapping relationships between pathology and genomics using samples with complete modalities, generating imputed features for missing samples to improve overall prognostic performance. VMEF comprises three core modules: (1) a multi-source pathology encoding module that fuses global tissue structure with tumor microenvironment prior information, providing a rich pathological foundation for genomic feature generation; (2) a VMoE-based imputation module that models diverse pathology-to-genomics mapping relationships through a dual-expert structure and dynamic routing mechanism, adaptively generating biologically plausible genomic representations; (3) a prior-guided fusion module that leverages prior features to guide mutual calibration between genomic features and pathological representations, effectively alleviating inter-modal heterogeneity. Experiments on three TCGA cancer datasets demonstrate that when only 60% of training samples have genomic sequencing data, the average C-index reaches 0.6149; under complete modality conditions, the average C-index reaches 0.6370, surpassing existing multimodal methods. The experimental results demonstrate the effectiveness and robustness of the VMEF framework for cancer prognosis under modality-missing scenarios, providing strong support for its application in randomly missing data scenarios.

摘要： 通过深度学习整合病理图像与基因组数据，可显著提升癌症预后预测的精准性。然而在临床数据中，仅有部分患者具备完整的基因组测序结果，这限制了多模态模型的全面应用。如何充分利用有限的基因数据增强病理模型的预后能力，是提升多模态临床适用性与泛化能力的关键。为此，本文提出一种基于变分专家混合（VMoE）模块的病理增强框架VMEF，用于应对病理图像完整但部分样本缺失基因数据的训练场景。该框架利用具备完整模态的样本学习病理基因间的跨模态映射关系，为缺失样本生成插补特征，从而提升整体预后性能。VMEF包含三个核心模块：（1）多源病理编码模块，融合全局组织结构与肿瘤微环境先验信息，为基因特征生成提供丰富的病理学基础；（2）基于VMoE的插补模块，通过双专家结构与动态路由机制建模病理到基因的多样映射关系，自适应生成生物学合理的基因表征；（3）先验特征引导的融合模块，通过先验特征引导基因特征与病理表示的相互校准，有效缓解模态间异质性。在TCGA三种癌症数据集上的实验表明，当训练集中仅60%样本具备基因测序数据时，平均C-index达到0.6149；在模态完整情况下，平均C-index达到0.6370并超越现有多模态方法。实验结果展现了VMEF框架在模态缺失情况下癌症预后的有效性与鲁棒性，为其在随机缺失场景中的应用提供了有力支撑。