A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer

Frontiers in Oncology

Oncology

Qin Lin¹  Qiang Li²  Juan Zhao²  Huoqiang Wang²  Yi Li²  Long Zhao²  Haohao Li³  Changlong Hu⁴  Jiexi Han⁵  Yiran Wang⁵ 

[1] Department of Geriatrics, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China;Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China;Faculty of Business and Economics, University of Hong Kong, Hong Kong, China;School of Life Sciences, Fudan University, Shanghai, China;Shanghai miRAN Biotech Co. Ltd, Shanghai, China;

关键词: PET/CT;    pulmonary nodule;    lung cancer;    diagnosis;    machine-learning;   

DOI  :  10.3389/fonc.2023.1192908

 received in 2023-03-24, accepted in 2023-09-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveThe aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data.MethodsA retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively.ResultsThe standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly.ConclusionThe feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Li, Han, Lin, Zhao, Li, Zhao, Li, Wang and Hu