基于医学影像的机器学习预测非小细胞肺癌EGFR突变的研究进展
Research progress in predicting EGFR mutation of NSCLC patients using machine learning based on medical imaging
孙元昕 1沈蕾蕾 2叶晓丹3
作者信息
- 1. 上海市影像医学研究所,上海 200032
- 2. 复旦大学附属中山医院放射科,上海 200032
- 3. 上海市影像医学研究所,上海 200032;复旦大学附属中山医院放射科,上海 200032;复旦大学附属中山医院肿瘤防治中心,上海 200032
- 折叠
摘要
随着计算机科学的迅速发展,人工智能在医学领域扮演了重要角色,基于影像学图片的机器学习在临床决策中发挥着重要的辅助作用,其与基因组学的深入结合为基因检测提供了新方法.本文主要论述基于医学影像的机器学习在预测非小细胞肺癌(non-small cell lung cancer,NSCLC)患者表皮生长因子受体(epidermal growth factor receptor,EGFR)突变中的研究现状、局限性以及未来发展趋势.
Abstract
With the rapid advancement of computer science,artificial intelligence has become an important role in the medical field.Machine learning,based on medical imaging,plays a significant complementary role in clinical decision-making.Moreover,the integration of imaging data with genomic information has introduced innovative avenues for genetic testing.The primary focus of this article was on the current state,limitations,and future trends of machine learning based on medical imaging for predicting epidermal growth factor receptor(EGFR)mutations in patients with non-small cell lung cancer(NSCLC).
关键词
机器学习/深度学习/影像组学/影像基因组学/非小细胞肺癌/表皮生长因子受体Key words
Machine learning/Deep learning/Radiomics/Radiogenomics/Non-small cell lung cancer/Epidermal growth factor receptor引用本文复制引用
基金项目
国家自然科学基金面上项目(82071990)
出版年
2024
NETL
NSTL
万方数据