Construction of an individualized brain metabolic network in patients with advanced non-small cell lung cancer by the Kullback-Leibler divergence-based similarity method: A study based on 18F-fluorodeoxyglucose positron emission tomography

Frontiers in Oncology

Oncology

Ying Wang¹  Qingling Chen¹  Jie Yu¹  Xiaoling Cao¹  Xinglin Zeng²  Lin Hua³  Zhen Yuan³ 

[1] Department of Nuclear Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Zhuhai, Guangdong, China;Faculty of Health Sciences, University of Macau, Macau, Macau SAR, China;Faculty of Health Sciences, University of Macau, Macau, Macau SAR, China;Centre for Cognitive and Brain Sciences, University of Macau, Macau, Macau SAR, China;

关键词: non-small cell lung cancer;    fluorodeoxyglucose;    positron emission tomography;    brain metabolic network;    Kullback-Leibler divergence-based similarity;   

DOI  :  10.3389/fonc.2023.1098748

 received in 2022-11-15, accepted in 2023-02-13,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundLung cancer has one of the highest mortality rates of all cancers, and non-small cell lung cancer (NSCLC) accounts for the vast majority (about 85%) of lung cancers. Psychological and cognitive abnormalities are common in cancer patients, and cancer information can affect brain function and structure through various pathways. To observe abnormal brain function in NSCLC patients, the main purpose of this study was to construct an individualized metabolic brain network of patients with advanced NSCLC using the Kullback-Leibler divergence-based similarity (KLS) method.MethodsThis study included 78 patients with pathologically proven advanced NSCLC and 60 healthy individuals, brain 18F-FDG PET images of these individuals were collected and all patients with advanced NSCLC were followed up (>1 year) to confirm their overall survival. FDG-PET images were subjected to individual KLS metabolic network construction and Graph theoretical analysis. According to the analysis results, a predictive model was constructed by machine learning to predict the overall survival of NSLCL patients, and the correlation with the real survival was calculated.ResultsSignificant differences in the degree and betweenness distributions of brain network nodes between the NSCLC and control groups (p<0.05) were found. Compared to the normal group, patients with advanced NSCLC showed abnormal brain network connections and nodes in the temporal lobe, frontal lobe, and limbic system. The prediction model constructed using the abnormal brain network as a feature predicted the overall survival time and the actual survival time fitting with statistical significance (r=0.42, p=0.012).ConclusionsAn individualized brain metabolic network of patients with NSCLC was constructed using the KLS method, thereby providing more clinical information to guide further clinical treatment.

【 授权许可】

Unknown   
Copyright © 2023 Yu, Hua, Cao, Chen, Zeng, Yuan and Wang