核,临床和遗传特征的整合用于肺癌亚型分类和基于机器和深度学习模型的生存预测
Bin Xie1, Mingda Mo1, Haidong Cui2
1School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.
Diagnostics (Basel, Switzerland)
|April 12, 2025
概括
这项研究开发了一种集核,临床和遗传数据的AI模型,用于准确的肺癌亚型分类和整体生存预测,实现高性能指标.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 人工智能在医学中的应用
背景情况:
- 肺癌是全球癌症相关死亡的主要原因之一.
- 准确的肺癌亚型分类和风险分层对于个性化治疗和患者管理至关重要.
- 现有的模型可能无法充分利用整合的病原体学,临床和遗传数据.
研究的目的:
- 开发和评估一个人工智能 (AI) 模型,用于精确的肺癌亚型分类.
- 建立一个有效的模型来预测肺癌患者的整体存活率 (OS).
- 研究整合核,临床和遗传特征的实用性,以提高预测准确度.
主要方法:
- 利用了来自癌症基因组图谱 (TCGA) 的组织病理学图像,临床数据和遗传信息,用于肺腺癌和肺状细胞癌.
- 优化了基于核,临床和遗传特征的影响因素系统.
- 使用并比较多个机器学习 (LightGBM,XGBoost,RF,AdaBoost) 和深度学习 (MLP,TabNet,CNN) 模型进行分类和操作系统预测.
主要成果:
- 在肺癌亚型分类中,XGBoost获得了0.9821的最高曲线下面面积 (AUC).
- 随机森林 (RF) 在OS预测方面表现优越,AUC为0.9134 (1年),0.8706 (2年) 和0.8765 (3年).
- 与之前的研究相比,综合方法显著提高了预测准确性.
结论:
- 这项研究是首次将核形态学和肺癌亚型和OS预测的遗传信息结合起来.
- 由人工智能驱动的多种数据类型的整合在预测肺癌结果方面取得了重大进展.
- 开发的模型显示了改善肺癌管理中的临床决策的前景.
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