Research progress in predicting<em> EGFR </em>mutation of NSCLC patients using machine learning based on medical imaging

SUN Yuanxin; SHEN Leilei; YE Xiaodan

doi:10.19732/j.cnki.2096-6210.2024.03.017

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Research progress in predicting EGFR mutation of NSCLC patients using machine learning based on medical imaging

更新时间：2025-12-15

- Research progress in predicting EGFR mutation of NSCLC patients using machine learning based on medical imaging
- Vol. 33, Issue 3, Pages: 323-329(2024)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2024.03.017
  CLC：
- Published Online：19 July 2024，
  
  Published：19 July 2024
- 稿件说明：
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孙元昕,沈蕾蕾,叶晓丹. 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[J]. 肿瘤影像学, 2024, 33(3): 323-329 https://doi.

org/10.19732/j.cnki.2096-6210.2024.03.017
孙元昕,沈蕾蕾,叶晓丹. 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[J]. 肿瘤影像学, 2024, 33(3): 323-329 https://doi. DOI： 10.19732/j.cnki.2096-6210.2024.03.017.

org/10.19732/j.cnki.2096-6210.2024.03.017 DOI：

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).

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Related Author

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Related Institution

Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University

Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University

Department of Ultrasound, Shanghai Punan Hospital of Pudong New District

College of Medical Instrumentation, Shanghai University of Medicine & Health Sciences

Department of Radiology, Huadong Hospital, Fudan University

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Research progress in predicting EGFR mutation of NSCLC patients using machine learning based on medical imaging

DOI：10.19732/j.cnki.2096-6210.2024.03.017

摘要

Abstract

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references