标题： PathoGen-X：一种用于从组织病理学图像增强生存预测的跨模态基因特征变换对齐网络 显示英文标题

标题： PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images

摘要： 准确的生存预测对于个性化癌症治疗至关重要。然而，基因组数据——通常比病理数据更强大的预测因子——既昂贵又难以获取。我们提出了跨模态基因组特征翻译与对齐网络（PathoGen-X），用于从组织病理学图像中增强生存预测。这是一种深度学习框架，在训练过程中利用基因组和成像数据，在测试时仅依赖成像数据。PathoGen-X 使用基于变压器的网络将图像特征对齐并翻译到基因组特征空间，并通过更强的基因组信号增强较弱的图像信号。与其他方法不同，PathoGen-X 在不将特征投影到共享潜在空间的情况下进行翻译和对齐，并且需要较少的配对样本。在 TCGA-BRCA、TCGA-LUAD 和 TCGA-GBM 数据集上评估时，PathoGen-X 展示了强大的生存预测性能，强调了丰富成像模型在可访问癌症预后中的潜力。 显示英文摘要

摘要： Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessible. We present the cross-modal genomic feature translation and alignment network for enhanced survival prediction from histopathology images (PathoGen-X). It is a deep learning framework that leverages both genomic and imaging data during training, relying solely on imaging data at testing. PathoGen-X employs transformer-based networks to align and translate image features into the genomic feature space, enhancing weaker imaging signals with stronger genomic signals. Unlike other methods, PathoGen-X translates and aligns features without projecting them to a shared latent space and requires fewer paired samples. Evaluated on TCGA-BRCA, TCGA-LUAD, and TCGA-GBM datasets, PathoGen-X demonstrates strong survival prediction performance, emphasizing the potential of enriched imaging models for accessible cancer prognosis.