多模式深度学习用于癌症预测预后与临床信息促使整合
Jiaxin Hou1,2, Ranran Zhang1, Yaoqin Xie1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
NPJ digital medicine
|December 27, 2025
概括
通过整合病理图像,基因组数据和临床记录,可以提高精确的癌症生存预测. 我们的SurvPGC模型有效地结合了这些不同的数据源,以获得更好的预后见解.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 医疗成像医学成像
背景情况:
- 准确的癌症生存预测对于治疗规划和疗效评估至关重要.
- 瘤异质性对准确的预后评估构成重大挑战.
- 综合成像,基因组学和临床数据的多式学习显示了癌症预后的前景.
研究的目的:
- 开发一个综合模型,SurvPGC,用于增强癌症预后.
- 有效地将未充分利用的临床记录与成像和基因组数据一起纳入.
- 通过利用多式联络数据,提高癌症生存预测的准确性.
主要方法:
- 拟议的SurvPGC模型整合了病理图像,基因组数据和临床记录.
- 使用文本模板和基础模型将临床信息转化为高维向量.
- 利用交叉注意模块来有效地整合各种数据模式.
主要成果:
- SurvPGC模型证明了在数据集中有效捕获模式特定特征.
- 注意力可视化揭示了不同数据类型的不同重点领域,表明有效的功能集成.
- 在三个癌症基因组图谱数据集的验证证实了该模型的性能.
结论:
- 整合各种数据来源,包括临床记录,显著改善了癌症存活率预测.
- SurvPGC模型为多模式癌症预后提供了一个强大的框架.
- 强调临床信息在提高预后准确性方面的关键作用.
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