在口腔白血病和头支状细胞癌中使用基于病理学的人工智能识别基因组变异和预后:一项多中心实验研究
Xin-Jia Cai1,2, Chao-Ran Peng2,3, Ying-Ying Cui2,3
1Central Laboratory, Peking University School and Hospital of Stomatology.
International journal of surgery (London, England)
|September 9, 2024
概括
这项研究开发了一种人工智能模型,使用病理图像预测口腔白血病和头癌中的9p染色体损失. 该模型有助于评估患者的预后,并可以改善临床管理.
科学领域:
- 在瘤学瘤学.
- 病理学 病理学 病理学
- 人工智能的人工智能
背景情况:
- 染色体9p的损失是口腔白血病 (OLK) 恶性转变为头支状细胞癌 (HNSCC) 的关键生物标志物.
- 对9p损失的临床评估具有挑战性,这阻碍了其在实践中的应用.
- 准确预测9p损失对于HNSCC的预后和管理至关重要.
研究的目的:
- 开发一种基于病理学的快速和经济高效的人工智能 (AI) 模型,用于预测9p损失 (9PLP).
- 为了能够从标准的H&E染色基因病理学图像直接预测9p损失.
- 评估AI在改善OLK和HNSCC的临床管理方面的潜力.
主要方法:
- 开发了一个深度学习模型 (9PLP),使用333个OLK病例的血素和乙素 (H&E) 染色的整个幻灯片图像.
- 该模型结合了Transformer和XGBoost算法,在多中心数据集上进行训练和验证.
- 人工智能模型在独立的HNSCC数据集上进一步验证,并与预后评估的临床病理参数集成.
主要成果:
- 9PLP模型准确地预测了OLK (AUC=0.890) 和HNSCC (AUC=0.825) 图像中的9p损失.
- 该模型在预测HNSCC患者预后方面表现出高准确性,AUC为0.739 (1年),0.705 (3年) 和0.691 (5年).
- 这代表了OLK和HNSCC中基因组改变预测的第一个深度学习模型.
结论:
- 开发的AI模型有效地预测9p损失,并使用H&E图像的病理特征评估HNSCC患者的预后.
- 9PLP模型显示了改善口腔白血病和头角状细胞癌的临床管理的潜力.
- 这种新的方法为将基因组信息整合到常规基因病理学分析中提供了一个有前途的工具.
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