从癌症组织病理学产生跨模式基因表达改善了多模式AI预测
Samiran Dey1, Christopher R S Banerji2,3, Partha Basuchowdhuri1
1School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science, Kolkata, India.
Nature communications
|December 31, 2025
概括
人工智能现在只能使用数字病理图像来预测癌症分级和生存风险. 这种新的方法合成了基因表达数据,为医疗保健中昂贵的转录组测试提供了切实可行的替代方案.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 多模式人工智能 (AI) 整合数字病理学和转录基因数据显示出癌症诊断和预后的前景.
- 临床实施受到阻碍,因为转录组数据在常规医疗保健环境中很少可用,基因病理学仍然是标准.
- 从数字病理学中合成转录组数据为实际的AI驱动癌症分析提供了潜在的解决方案.
研究的目的:
- 开发和验证一种人工智能模型,该模型可以从数字组织病理学图像中合成转录组数据,用于癌症分级和生存风险预测.
- 评估使用合成转录基因数据与真实转录基因数据进行的预测的准确性和可靠性.
- 为了证明基于人工智能的多式融合的临床可行性,而不需要实际的转录组测量.
主要方法:
- 利用两个公共的多模式癌症数据集 (TCGA,CPTAC) 跨越四个队伍:质瘤-质母细胞瘤,脏,子宫和乳腺.
- 开发了一种基于扩散的交叉模式生成AI模型PathGen,用于从整个幻灯片图像 (WSIs) 中合成基因表达数据.
- 评估模型在预测癌症分级和患者生存风险方面的表现,确保确定性和可解释性.
主要成果:
- 将合成的转录基因数据与WSIs相结合,显著改善了癌症分级和风险估计 (p < 0.05).
- 使用合成特征的预测在统计学上与使用真实转录组数据 (p > 0.05) 的预测在所有队列中都是可比的.
- 在共同预测癌症分级和生存风险方面,PathGen实现了先进的性能,具有高准确性和确定性.
结论:
- 来自数字病理学的基因表达的人工智能驱动合成是癌症分级和生存风险预测的可行和准确方法.
- "PathGen"为瘤学领域的多式人工智能提供了一种实用,具有成本效益的方法,克服了转录基因数据采集的局限性.
- 该模型提供可解释的预测和确定性保证,为临床整合铺平了道路.
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