The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.

中文翻译：

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利用辅助临床变量和不完整的多模态数据进行癌症预后分析的关系感知共享表示学习


多模态数据（例如组织病理学图像和基因组数据）中包含的补充表型信息的综合分析推进了癌症的预后评估。然而，基于多模态的预后分析面临两个挑战：（1）如何探索不同模态数据固有的潜在关系，以学习紧凑且有区别的多模态表示； （2）由于大量完整的多模态数据并不总是可用，如何充分考虑不完整的多模态数据来构建准确且稳健的预后模型。此外，许多现有的基于多模态的预后方法通常忽略相关的临床变量（例如，分级和阶段），然而，这些变量可以提供补充信息以促进模型的性能。在本文中，我们提出了一种用于癌症预后分析的关系感知共享表示学习方法，该方法充分利用临床信息和不完整的多模态数据。所提出的方法通过对偶映射学习为预后模型定制的多模态共享空间。在共享空间内，它配备了关系正则化器来探索多模态数据之间的潜在关系（即特征标签和特征特征关系），以诱导歧视性表示，同时获得额外的稀疏性以减轻过度拟合。此外，它还回归并结合了多个辅助临床属性和动态系数以改善性能。此外，在训练阶段，采用部分映射策略来扩展和训练具有不完整的多模态数据的更可靠的模型。 我们在来自癌症基因组图谱（TCGA）项目的三个公共数据集上评估了我们的方法，实验结果证明了所提出方法的优越性能。