EPISODE · May 24, 2026 · 23 MIN
1025-AI病理预测乳腺癌空间转录组学与生物标志物发现
from 聊聊Sci
本文介绍了一种名为 Path2Space 的深度学习模型，它能够直接从常规的 H&E 染色病理切片中预测成千上万个基因的空间表达谱。该研究通过将低成本的组织学图像与高价的空间转录组学技术相结合，成功克服了传统分子检测成本高昂且难以大规模应用的局限性。Path2Space 不仅能精准刻画肿瘤微环境并识别具有不同生存预后的临床亚群（即 SpatioTypes），还能比传统的批量测序更有效地预测患者对化疗和曲妥珠单抗的治疗反应。这一创新工具为发现大规模临床队列中的空间生物标志物提供了可扩展且经济高效的方案，对癌症的精准医疗具有重大意义。References:Shulman E D, Campagnolo E M, Lodha R, et al. AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology[J]. Cell, 2026.前往小宇宙评论区与主播互动
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1025-AI病理预测乳腺癌空间转录组学与生物标志物发现
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