通过无细分表示学习对瘤异质性的表征
bioRxiv : the preprint server for biology
|September 16, 2024
概括
我们开发了CANVAS,一种新的自我监督学习框架,用于分析复杂的瘤微环境 (TME) 图像. CANVAS确定了新的TME类型和肺瘤中预后不佳的单细胞特征.
科学领域:
- 计算生物学是一种计算生物学.
- 癌症研究 癌症研究
- 在病理学中的人工智能.
背景情况:
- 瘤微环境 (TME) 的相互作用是复杂和异质的.
- 高维复合成像提供了对瘤空间组织的洞察.
- 分析大型,复杂的TME图像以进行发现仍然是一个重大挑战.
研究的目的:
- 推出CANVAS,一个自我监督的代表性学习框架,用于TME发现.
- 为了能够识别和描述新型的TME亚型.
- 克服传统基于细分的空间分析方法的局限性.
主要方法:
- 视觉转换器CANVAS使用自主监督的蒙面图像建模在高维多重复图像上.
- 该框架没有细分,直接运行在像素级数据上.
- 它保留了局部形态和生物标志物分布,以便进行详细分析.
主要成果:
- 帆布有效地区分微妙的形态差异和生物标志物模式.
- 该框架精确地分离和表征不同的TME签名.
- 对肺瘤数据集的应用确定了一种新的单细胞TME签名.
结论:
- CANVAS为TME分析提供了一种强大的,没有细分的方法.
- 确定的单细胞特征与肺癌的预后不佳有关.
- 这一框架有助于发现新的TME亚型及其临床相关性.
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02:07Tumor Progression
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...