发表机构
Universitat Politècnica de Catalunya; IRB Lleida（加泰罗尼亚理工大学; 莱里达生物医学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对胃腺癌免疫治疗RNA特征检测成本高的问题，提出VITA模型，通过H&E与RNA跨模态对齐实现仅用H&E切片近似RNA特征，取得0.72准确率等结果，提供低成本预筛选工具。
AI 中文摘要
识别胃腺癌中的“炎症”免疫表型可预测免疫治疗应答，但需要昂贵的10基因RNA特征。尽管对标准H&E切片进行深度学习提供了可扩展的替代方案，但传统二分类器过度简化了连续RNA数据并引入标签噪声。为解决这一问题，我们提出VITA（Virtual Transcriptomic Approximation，虚拟转录组近似）。通过在训练期间将H&E与RNA对齐到联合潜在空间，VITA在推理时仅需标准H&E切片即可检索形态相似的历史病例并近似连续RNA特征。VITA取得了0.72的分类准确率和0.66的斯皮尔曼相关系数，提供了一种具有成本效益的“虚拟转录组学”预筛选工具，无需基因组测序即可保留连续表型谱。
英文摘要
Identifying the ``Inflamed'' immunophenotype in Gastric Adenocarcinoma predicts immunotherapy response but requires an expensive 10-gene RNA signature. While deep learning on standard H\&E slides offers a scalable alternative, conventional binary classifiers oversimplify continuous RNA data and introduce label noise. To resolve this, we propose VITA (VIrtual Transcriptomic Approximation). By aligning H\&E and RNA into a joint latent space during training, VITA requires only standard H\&E at inference to retrieve morphologically similar historical cases and approximate the continuous RNA signature. Achieving 0.72 classification accuracy and a 0.66 Spearman correlation, VITA provides a cost-effective ``virtual transcriptomics'' pre-screening tool that preserves the continuous phenotypic spectrum without requiring genomic sequencing.
CommentsAccepted to MIDL 2026 Short Paper track
