Relation Between Imaging Microfeatures of Artificial Intelligence-assisted Diagnosis System and Prognosis of Lung Adenocarcinomas Presented as Ground-glass Nodules

WEI Ning; LIN Ruijiang; MA Minjie; CHEN Chang; HAN Biao

doi:10.3971/j.issn.1000-8578.2021.21.0255

Cancer Research on Prevention and Treatment > 2021 > 48(9): 877-882. > DOI: 10.3971/j.issn.1000-8578.2021.21.0255

WEI Ning, LIN Ruijiang, MA Minjie, CHEN Chang, HAN Biao. Relation Between Imaging Microfeatures of Artificial Intelligence-assisted Diagnosis System and Prognosis of Lung Adenocarcinomas Presented as Ground-glass Nodules[J]. Cancer Research on Prevention and Treatment, 2021, 48(9): 877-882. DOI: 10.3971/j.issn.1000-8578.2021.21.0255

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Relation Between Imaging Microfeatures of Artificial Intelligence-assisted Diagnosis System and Prognosis of Lung Adenocarcinomas Presented as Ground-glass Nodules

1.
Department of Thoracic Surgery, The First Hospital of Lanzhou University, Key Technologies and Applications of Thoracic Surgery in Gansu Province International Cooperation Base, Lanzhou 730000, China
2.
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China

Funding:

Youth Science and Technology Fund of Gansu Province 18JR3RA305

More Information

Corresponding author:
HAN Biao, E-mail: hanbiao66@163.com
Received Date: March 08, 2021
Revised Date: May 12, 2021
Available Online: January 12, 2024

Graphical Abstract

Abstract

Abstract

Objective
To investigate the relation between the imaging microfeatures of AI-assisted diagnosis system and the prognosis of lung adenocarcinomas presented as ground-glass nodules (GGN).
Methods
We retrospectively analyzed CT data of 162 patients with lung adenocarcinomas presented as GGN. According to different imaging characteristics, the patients were divided into pure ground glass nodules (PGGN) group and mixed ground glass nodules (MGGN) group. The AI-assisted diagnosis system was used to extract their imaging microfeatures, and their relation with the prognosis of the patients was analyzed.
Results
The five-year OS and RFS were 89.7% and 88.5% in PGGN group, and 81.0% and 79.0% in MGGN group (χ²=6.289/7.255, P < 0.05). Multivariate Cox regression showed that imaging microfeatures such as microvascular cluster (P < 0.001), standard nodule volume (P=0.013) and nodule length (P < 0.001) were independent risk factors for OS, meanwhile, imaging microfeatures such as microvascular cluster (P < 0.001), standard nodule volume (P=0.017), nodule length (P=0.005), nodule central density (P=0.038) and lymph node metastasis (P < 0.001) were independent risk factors for RFS.
Conclusion
The AI-assisted diagnosis system can effectively predict the prognosis of lung adenocarcinomas presented as GGN, and it also has a certain reference value for the clinical precision diagnosis and treatment of GGN and the prevention and treatment of early lung cancer.
- Artificial intelligence,
- Ground-glass nodules,
- Adenocarcinoma,
- Imaging microfeatures

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Competing interests: The authors declare that they have no competing interests.

References (19)

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Relation Between Imaging Microfeatures of Artificial Intelligence-assisted Diagnosis System and Prognosis of Lung Adenocarcinomas Presented as Ground-glass Nodules