病态特征揭示了免疫和分子进化,从肺部预新生病到侵入性腺癌
Pingjun Chen1, Frank R Rojas2, Xin Hu3
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
概括
这项研究使用人工智能从例行病理学幻灯片中分析肺癌前体细胞. 人工智能病理学揭示了肺癌早期发育过程中的关键细胞变化,提供了一个具有成本效益的研究工具.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 数字健康数字健康
背景情况:
- 由于早期诊断的增加,肺癌,特别是腺癌 (ADC),需要了解早期的致癌发生.
- 非典型腺瘤性增生 (AAH) 是唯一被认可的ADC前体,通过局部腺癌 (AIS) 和微创性腺癌 (MIA) 进展.
- 对早期肺癌发生的研究受到前体组织样本稀缺的阻碍.
研究的目的:
- 用人工智能 (AI) 和深度学习对例行病理学图像来解码肺部 preneoplasia 的演变.
- 识别和分析从正常肺组织到侵袭性ADC的进展过程中细胞变化的病态特征.
- 证明病理学的实用性,作为研究肺癌发展的分子分析的经济有效替代方案.
主要方法:
- 利用深度学习和人工智能来细分和识别血素和素 (H&E) 染色基因病理学图像中的细胞.
- 从三个国际队列 (日本,中国,美国) 的正常肺组织,AAH,AIS,MIA和ADC中提取了9个与生物学相关的病态特征.
- 分析了98名患者,162个幻灯片和669个感兴趣区域的数据.
主要成果:
- 病态特征显示非典型上皮细胞的逐渐增加和淋巴细胞的减少从正常到ADC阶段.
- 细胞内瘤异质性在癌症发生谱中逐渐增加,从正常肺到侵入性ADC.
- 人工智能驱动的病态分析结果与昂贵的分子和免疫分析结果一致.
结论:
- 在常规的H&E幻灯片上应用的Pathomics有效地解码了肺 preneoplasia 中的细胞进化.
- 以人工智能为基础的病态特征提取为研究肺癌致癌提供了一种可行的和强大的方法.
- 这种方法为利用可访问的病理学数据推进早期肺癌发展研究提供了巨大的潜力.
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