Prognostic and Predictive Values of Metabolic Parameters of F-FDG PET/CT in Patients With Non-Small Cell Lung Cancer Treated With Chemotherapy

Abstract

Objectives: Increasing interests have been focused on using artificial intelligence (AI) to extend prognostic value of medical imaging. Feature extraction is a critical step for successful application of AI. The aim of this study was to explore several metabolic parameters measured by 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) as potential AI features in predicting the effectiveness of chemotherapy in patients with non-small cell lung cancer (NSCLC). Methods: A set of metabolic parameters of PET/CT and clinical characteristics were detected from 137 patients with NSCLC treated with at least 1 cycle of chemotherapy. Survival receiver–operating characteristic (ROC) analysis was used to define the more significant parameters chosen for the following survival analysis. Patient survival was analyzed by Kaplan-Meier method, log-rank test, and Cox regression. Results: Survival ROC showed that maximum standardized uptake value (SUVmax), metabolic tumor volume 50% (MTV50), and total lesion glycolysis 50% (TLG50) had larger area under the curve, and the optimal cutoff values were 11.72, 4.04, and 34.55, respectively. Univariate and multivariate analyses synergistically showed that late PET/CT stage and MTV50 >4.04 were independent factors of poor survival in patients with NSCLC who received chemotherapy. Conclusions: Several potential prognostic biomarkers of PET/CT imaging have been extracted for predicting survival and selecting patients with NSCLC who are more likely to benefit from chemotherapy. The identification may accelerate the development of AI methods to improve treatment outcome for NSCLC.