The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Qiuyuan Hu; Ke Li; Conghui Yang; Yue Wang; Rong Huang; Mingqiu Gu; Yuqiang Xiao; Yunchao Huang; Long Chen

doi:10.3389/fonc.2023.1133164

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Front Oncol. 2023 Mar 7:13:1133164. doi: 10.3389/fonc.2023.1133164. eCollection 2023.

Authors

Qiuyuan Hu¹, Ke Li², Conghui Yang¹, Yue Wang¹, Rong Huang¹, Mingqiu Gu¹, Yuqiang Xiao¹, Yunchao Huang³, Long Chen¹

Affiliations

¹ Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China.
² Department of Cancer Biotherapy Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China.
³ Department of Thoracic Surgery I, Key Laboratory of Lung Cancer of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China.

Abstract

Objectives: Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).

Materials and methods: A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.

Results: Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.

Conclusion: AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

Keywords: NSCLC; PET/CT; artificial intelligence; lung cancer; radiomics.

Publication types

Review

Grants and funding

This study was supported by the National Natural Science Foundation of China (81960496), Yunnan Fundamental Research Projects (202101AT070050, 202201AY070001-155), Yunnan health training project of high-level talents (H-2018006, D-2017001), Xing Dian Ying Cai Support Plan, Leading Talents of Yunnan Health System (L-201206), Joint Laboratory of International Cooperation of the Ministry of Education for Regional High incidence Cancer at High altitude (K1322215), Science and Technology Innovation Team of Diagnosis and Treatment for Glucolipid Metabolic Diseases in Kunming Medical University (CXTD202106), and Yunnan Cancer Clinical Medical Center (ZX2019-05-01).