Application progress of deep learning techniques in lung cancer imaging diagnosis

SUN Yao; WANG Xiang; XIAO Yi

doi:10.19732/j.cnki.2096-6210.2021.06.016

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Application progress of deep learning techniques in lung cancer imaging diagnosis

更新时间：2025-12-15

- Application progress of deep learning techniques in lung cancer imaging diagnosis
- Vol. 30, Issue 6, Pages: 525-531(2021)
- 作者机构：
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- DOI：10.19732/j.cnki.2096-6210.2021.06.016
  CLC：
- Published Online：28 December 2021，
  
  Published：28 December 2021
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孙瑶,王祥,萧毅. 深度学习技术在肺癌影像学诊断中的应用进展[J]. 肿瘤影像学, 2021, 30(6): 525-531 https://doi.

org/10.19732/j.cnki.2096-6210.2021.06.016
孙瑶,王祥,萧毅. 深度学习技术在肺癌影像学诊断中的应用进展[J]. 肿瘤影像学, 2021, 30(6): 525-531 https://doi. DOI： 10.19732/j.cnki.2096-6210.2021.06.016.

org/10.19732/j.cnki.2096-6210.2021.06.016 DOI：

摘要

在全球范围内，肺癌的发病率和死亡率居恶性肿瘤的首位，在中国，随着人口老龄化的进程，肺癌已成为严重危害人民生活健康的重要疾病之一，中晚期肺癌5年生存率仅有15%左右，早发现、早诊断、早治疗是提高肺癌生存率和降低死亡率的关键。近年来，人工智能技术快速发展，已进入到临床诊疗环节，其中肺癌影像诊断是人工智能技术发展最快、相对成熟的领域，人工智能在肺癌诊疗中发挥着重要作用。人工智能辅助影像学数据深度挖掘的应用涵盖了肺癌诊疗环节的方方面面，在图像重建、肺结节检出、肺结节分类、结节测量、疗效评估及组织病理学诊断等领域都有了长足的进步。本文从人工智能在肺癌影像学诊断中的分割、分类、疗效评估及图像重建等方面的进展进行重点阐述，以期更好地认识和推广人工智能技术在肺部影像学诊断中的应用。

Abstract

Globally

the incidence and mortality of lung cancer ranks first among malignant tumors. In China

with the aging of population

lung cancer has become one of the important diseases that seriously harm peoples life and health. For years

the 5-year survival rate of advanced lung cancer was only about 15%. Early detection

early diagnosis and early treatment are the key to improve the survival rate and reduce the mortality of lung cancer. In recent years

artificial intelligence techniques have developed rapidly and have been applied in clinical diagnosis and treatment. Among them

the field of lung cancer imaging diagnosis is the fastest developing and relatively mature field of artificial intelligence technology

which plays an important role in the diagnosis and treatment of lung cancer. The application of deep mining of image data by artificial intelligence aided covers all aspects of lung cancer diagnosis and treatment

and has made great progress in image reconstruction

pulmonary nodule detection

pulmonary nodule classification

nodule measurement

efficacy evaluation

histopathological diagnosis and other fields. This paper focused on the progress of artificial intelligence in lung cancer image segmentation

classification

efficacy evaluation and image reconstruction

so as to better understand and promote the application of artificial intelligence technology in lung cancer.

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Related Institution

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

Department of Radiology, 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

Collaborative Innovation Center for Molecular Imaging of Precision Medicine Shanxi Medical University

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