Survival prediction for esophageal squamous cell cancer (ESCC) is crucial for doctors to assess a patient's condition and tailor treatment plans. The application and development of multi-modal deep learning in this field have attracted attention in recent years. However, the prognostically relevant features between cross-modalities have not been further explored in previous studies, which could hinder the performance of the model. Furthermore, the inherent semantic gap between different modal feature representations is also ignored. In this work, we propose a novel autoencoder-based deep learning model to predict the overall survival of the ESCC. Two novel modules were designed for multi-modal prognosis-related feature reinforcement and modeling ability enhancement. In addition, a novel joint loss was proposed to make the multi-modal feature representations more aligned. Comparison and ablation experiments demonstrated that our model can achieve satisfactory results in terms of discriminative ability, risk stratification, and the effectiveness of the proposed modules.

本研究解决了食管鳞状细胞癌（ESCC）生存预测中跨模态特征关联不足的问题。提出了一种基于自编码器的新深度学习模型，设计了多模态特征增强和建模能力提升的模块，并引入了新的联合损失函数，以提高特征表示的一致性。实验结果表明，该模型在辨别能力和风险分层方面表现优越，具有显著的临床应用潜力。

多模态中间特征交互自编码器用于食管鳞状细胞癌的总体生存预测