Screen Them All：基于H&E全切片图像的高通量泛癌症遗传与表型生物标志物筛查
Screen Them All: High-Throughput Pan-Cancer Genetic and Phenotypic Biomarker Screening from H&E Whole Slide Images
AI总结：
OmniScreen利用60,529名患者的H&E全切片图像和Virchow2嵌入，通过统一AI模型高通量预测跨癌症的489基因生物标志物，为临床筛查提供快速经济方案。
摘要：
分子检测是癌症预后和治疗选择中检测基因组改变的标准护理方法，但成本高、破坏组织且耗时。应用于常规苏木精和伊红（H&E）染色全切片图像（WSIs）的人工智能（AI）为筛查分子生物标志物提供了一种快速且经济的替代方案。我们引入了OmniScreen，这是一个基于AI的高通量系统，利用从60,529名癌症患者中提取的Virchow2嵌入，这些患者具有配对的489基因MSK-IMPACT靶向生物标志物组合和WSIs。与为每个生物标志物训练单独模型的传统方法不同，OmniScreen采用统一模型来预测跨多种癌症的广泛临床相关生物标志物，包括难以单独建模的低患病率靶点。OmniScreen能可靠地识别常见和罕见肿瘤的治疗靶点及共享表型特征。我们研究了OmniScreen的生物标志物预测概率和准确性与肿瘤区域、队列规模、组织学亚型一致性及通路水平形态模式的关系。这些发现强调了OmniScreen用于常规临床筛查的潜力。
英文摘要：
Molecular assays are standard of care for detecting genomic alterations in cancer prognosis and therapy selection but are costly, tissue-destructive and time-consuming. Artificial intelligence (AI) applied to routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) offers a fast and economical alternative for screening molecular biomarkers. We introduce OmniScreen, a high-throughput AI-based system leveraging Virchow2 embeddings extracted from 60,529 cancer patients with paired 489-gene MSK-IMPACT targeted biomarker panel and WSIs. Unlike conventional approaches that train separate models for each biomarker, OmniScreen employs a unified model to predict a broad range of clinically relevant biomarkers across cancers, including low-prevalence targets impractical to model individually. OmniScreen reliably identifies therapeutic targets and shared phenotypic features across common and rare tumors. We investigate the biomarker prediction probabilities and accuracies of OmniScreen in relation to tumor area, cohort size, histologic subtype alignment, and pathway-level morphological patterns. These findings underscore the potential of OmniScreen for routine clinical screening.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
