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Jeya Maria Jose Valanarasu¹, Hanwen Xu², Naoto Usuyama¹

¹Microsoft Research, Redmond, WA, USA.

|December 10, 2025

概括

GigaTIME使用人工智能从标准的H&E幻灯片中创建虚拟多重免疫光图像,使许多癌症类型和患者的大规模瘤免疫微环境 (TIME) 分析成为可能.

科学领域:

计算病理学
在瘤学中使用人工智能
癌症免疫学

背景情况:

瘤免疫微环境 (TIME) 影响癌症的进展和治疗结果.
多重体免疫光 (mIF) 对于 TIME 分析是有价值的,但成本高且吞吐量低.
由于数据的局限性,现有的方法难以进行大规模的 TIME 描述.

研究的目的:

开发GigaTIME,用于人口规模的时间建模的AI框架.
通过交叉模式的翻译来弥合细胞形态和状态.
从H&E幻灯片生成虚拟的mIF图像进行广泛的分析.

主要方法:

在400万个细胞上训练了一种交叉模式的翻译器,并配对了H&E和mIF数据 (21种蛋白质).
在24种癌症类型的14256名患者中应用了GigaTIME,生成了299376个虚拟mIF幻灯片.
在10200名TCGA患者的验证结果.

主要成果:

创建了一个大型虚拟队列的时间数据.
发现了蛋白质,生物标志物,阶段和生存之间的1234个显著关联.
证明了以前由于mIF数据稀缺而受到限制的大规模TIME分析的可行性.

结论:

通过使用标准的H&E幻灯片,GigaTIME可以进行可扩展,具有成本效益的TIME分析.
人工智能框架显著扩大了TIME研究的范围.
这种方法有助于发现新生物标志物和治疗点.

关键词:

数字病理学多式联运AI 精确的健康现实世界数据现实世界证据空间蛋白质组学瘤微环境虚拟的人口

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