Lili Yuan,1,* Lin An,1,* Yandong Zhu,1 Chongling Duan,1 Weixiang Kong,1 Pei Jiang,2 Qing-Qing Yu1 

1Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China; 2Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Qing-Qing Yu, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, 272000, People’s Republic of China, Email yuqingqing_lucky@163.com Pei Jiang, Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, 272000, People’s Republic of China, Email jiangpeicsu@sina.com

Abstract: As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

Keywords: machine learning, computed tomography, lung cancer, artificial intelligence, diagnosis