Application of radiomics in diagnosis and treatment of lung cancer

Feng Pan; Li Feng; Baocai Liu; Yue Hu; Qian Wang

doi:10.3389/fphar.2023.1295511

Application of radiomics in diagnosis and treatment of lung cancer

Front Pharmacol. 2023 Nov 1:14:1295511. doi: 10.3389/fphar.2023.1295511. eCollection 2023.

Authors

Feng Pan^#^{1

2}, Li Feng^#¹, Baocai Liu¹, Yue Hu³, Qian Wang¹

Affiliations

¹ Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
² Department of CT, Jilin Province FAW General Hospital, Changchun, China.
³ Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China.

^# Contributed equally.

Abstract

Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.

Keywords: NSCLC; artificial intelligence; diagnosis; lung cancer; radiomics.

Publication types

Review

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by grants from the National Nature Science Foundation of China (Grant No.82003208), Science and Technology Project of Jilin Province Education Department (Grant no. JJKH20221069KJ) and the Natural Science Foundation of Jilin Province, China (Grant No.212558JC010284904), Project of Science and Technology Department of Jilin Province (grant number 20190303151SF, 20210401138YY, 20230203082SF).