基于人工智能的放射基因组学的进展,用于肺癌精准医学
Yanqi Sun1, Xiayao Guo1, Xiaohui Liu1
1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, Jiangsu Province, People's Republic of China.
Progress in biomedical engineering (Bristol, England)
|November 20, 2025
概括
人工智能 (AI) 放射基因组学集成成像,基因组学和临床数据,用于非侵入性肺癌突变预测. 这种方法有助于个性化医疗,通过将成像特征与基因表达相关联,并预测患者的结果.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 生物信息学是一种生物信息学.
背景情况:
- 肺癌的精准医学需要整合不同的数据类型.
- 放射基因组学提供了一种非侵入性方法,将成像表型与基因组信息联系起来.
- 人工智能 (AI) 对于分析复杂的放射基因组数据至关重要.
研究的目的:
- 探索基于人工智能的放射基因组学在肺癌精密医学中的作用.
- 用放射基因组学研究癌症驱动突变的预测.
- 评估放射基因组学在开发肺癌预后和预测模型方面的潜力.
主要方法:
- 医疗成像,基因组学和临床数据的整合.
- 机器学习和深度学习技术的应用用于突变状态预测.
- 放射性特征与基因表达/基因群之间的相关性分析.
主要成果:
- 人工智能放射基因组学能够对关键的瘤基因突变 (例如EGFR,KRAS) 进行非侵入性预测.
- 在放射性特征和基因集群之间发现了显著的相关性,揭示了瘤异质性.
- 放射基因组学在预测治疗反应,复发和生存方面显示出潜力.
结论:
- 人工智能驱动的放射基因组学是个性化肺癌管理的一个有前途的工具.
- 需要进一步的研究来应对标准化,验证,可解释性和数据多样性等挑战.
- 人工智能和多omics数据库的进步将增强放射基因组模型的临床实用性.
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