用病理切片预测癌症免疫治疗反应，免去昂贵基因检测。
Zero-Cost Virtual RNA: Approximating Immunotherapy Signatures via Cross-Modal WSI Retrieval
- 通过联合嵌入将病理切片与基因数据对齐，实现无损映射。
- 在连续基因谱上达到0.66相关性，分类准确率达0.72。
- 适合需要低成本预筛免疫治疗响应的临床研究者。
胃腺癌中识别“炎症型”免疫表型可预测免疫治疗响应，但需依赖昂贵的10基因RNA谱。尽管基于标准H&E切片的深度学习提供了可扩展替代方案，传统二分类器会简化连续的RNA数据并引入标签噪声。为此，我们提出VITA（VIrtual Transcriptomic Approximation）。通过在训练中将H&E与RNA对齐至联合隐空间，VITA仅需标准H&E切片即可检索形态相似的历史病例，从而近似连续的RNA谱。在测试中实现0.72分类准确率和0.66斯皮尔曼相关性，提供一种无需基因测序即可保留连续表型谱的低成本‘虚拟转录组学’预筛工具。
原文摘要 · Abstract (English)
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.
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