The association between 18F-fluorodeoxyglucose PET intratumoral metabolic heterogeneity and pathological parameters in non-small cell lung cancer. Issue 10 (October 2019)

• Record Type: Journal Article
• Authors: Li, Rui; Lin, Jie; Wang, Ling; Zheng, Xiangwu; Tang, Kun
• Is Part Of: Nuclear medicine communications. Volume 40:Issue 10(2019:Oct.)
• Publication Date: 2019-10
• Subjects: 18F-fluorodeoxyglucose -- heterogeneity -- non-small cell lung cancer -- PET; Nuclear medicine -- Periodicals; 616.07575
• DOI: 10.1097/MNM.0000000000001058
• Languages: English
• ISSNs: 0143-3636

Abstract

Abstract : Objective: The aim of this study was to evaluate the association between intratumoral metabolic heterogeneity on 18 F-fluorodeoxyglucose ( 18 F-FDG) PET imaging and the pathological type, differentiation and T stage of non-small cell lung cancer (NSCLC). Methods: Two hundred thirty-four consecutive patients who were performed pretreatment 18 F-FDG PET/computed tomography and pathologically diagnosed as NSCLC were assessed retrospectively. Metabolic heterogeneity factor (MHF) derived from the volume-threshold function from 40 to 90% was calculated for primary lesions. The association between MHF and the pathological type, differentiation and T stage of NSCLC were analyzed, respectively. Results: The MHF values were statistically different among pathological types, differentiation and T stages ( F = 17.611, 23.932 and 11.082, P all < 0.001). MHF showed a stepwise increase with the reduced degree of differentiation and increase of T stage. The trend analysis presented a strong positive relationship between MHF and T stage ( P < 0.05), but a correlated inversely and strongly relationship was demonstrated between MHF and diffferentiation ( P < 0.001). Moreover, multivariate ordinal logistic regression analysis showed that MHF was independently associated with tumor T stage and differentiation after adjusting for clinical factors. Conclusion: MHF showed high association with pathological parameters, which can be used to accurately predict the biological behaviors of tumors from the molecular level thus to provide important biological information for tumor diagnosis and treatment.