The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease. Issue 1 (25th November 2022) - Chen, Yu-Hung; Lue, Kun-Han; Chu, Sung-Chao; Chang, Bee-Song; Lin, Chih-Bin
Abstract : Objective: To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease. Methods: We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival. Results: Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies. Conclusion: Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.