Prognostic value of serum cholinesterase and 18F-FDG PET/CT-derived metabolic parameters in non-small cell lung cancer patients: a retrospective cohort study

医学 回顾性队列研究 肺癌 队列 内科学 肿瘤科 癌症 放射科 核医学
作者
Na Hu,Gang Yan,Maowen Tang,Shengmei Yuan,Xing Xia,Pinggui Lei
出处
期刊:Nuclear Medicine Communications [Lippincott Williams & Wilkins]
卷期号:46 (4): 337-346
标识
DOI:10.1097/mnm.0000000000001952
摘要

The primary objective of this study was to explore the prognostic significance of serum cholinesterase (CHE) and metabolic parameters obtained from 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) scans in patients with non-small cell lung cancer (NSCLC). A retrospective observational cohort study was conducted with 202 NSCLC patients. Serum CHE was evaluated alongside metabolic tumor volume (MTV) and total lesion glycolysis (TLG) derived from PET/CT scans. The correlation between these parameters and overall survival (OS) was analyzed using log-rank tests, as well as univariate and multivariate Cox regression analyses. A nomogram prediction model was developed and assessed using time-dependent receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). High MTV (≥16) and TLG (≥108) were found to be significantly correlated with worse OS outcomes (both P < 0.001), whereas lower CHE levels (<6818) were associated with worse OS ( P = 0.002). A multivariate analysis revealed that MTV, TLG, serum CHE, and the presence of distant metastasis were independent prognostic factors for OS. The nomogram prediction model, incorporating these variables, exhibited strong predictive performance, as indicated by area under the curve values of 0.826, 0.796, and 0.845 for 1-, 3-, and 5-year OS predictions, respectively. Calibration curves demonstrated good concordance between predicted and observed survival rates, and DCA confirmed clinical relevance. Serum CHE and 18 F-FDG PET/CT metabolic parameters may serve as important prognostic indicators for patients with NSCLC. The integration of these factors into a nomogram prediction model can assist in clinical decision-making and patient risk stratification.

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