用病理图像预测癌症生存期，让影像数据学会基因特征。
PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images
- 用Transformer将病理图像特征映射到基因特征空间，实现跨模态对齐。
- 在三个癌种数据集上表现优异，显著提升影像数据的预测能力。
- 无需共享潜在空间，少样本即可训练，适合基因数据难获取场景。
精准生存预测对个性化癌症治疗至关重要。然而，基因数据虽更具预测力，却成本高且难获取。本文提出基于跨模态基因特征转换与对齐的病理图像生存预测框架 PathoGen-X。该深度学习模型在训练阶段融合基因与影像数据，推理时仅需影像数据。PathoGen-X采用Transformer网络，将图像特征映射至基因特征空间，利用更强的基因信号增强较弱的影像信号。不同于现有方法，其不需将特征投影至共享潜空间，且所需配对样本更少。在 TCGA-BRCA、TCGA-LUAD 与 TCGA-GBM 数据集上的评估表明，PathoGen-X 在生存预测任务中表现强劲，凸显了强化影像模型在可及性癌症预后中的潜力。
原文摘要 · Abstract (English)
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.
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