Abstract
424
Objectives Textural features of tumoral uptake in fluorine 18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) images have been revealed to provide prognostic information in various tumors. We compared the prognostic value of metabolic parameters and tumor stage with texture features determined using 18F-FDG PET images in non-small cell lung cancer (NSCLC).
Methods Eighty-three patients (mean age, 63,4; 51 men, 32 women) with NSCLC underwent pretreatment 18F-FDG PET/CT scans were included in this study. Various metabolic or volume-based PET parameters including maximum and mean standardized uptake values (SUVmax and SUVmean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured in primary lung tumors. MATLAB technical computing language was employed in the extraction of texture features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters in three dimensional 18F-FDG PET images. The median follow-up among survivors was 21 months from the PET/CT (range 2-78 months). The analysis for overall survival (OS) was performed with a Kaplan-Meier method using PET metabolic parameters, tumor stage and texture features for determining independent prognostic factors, Cox regression analysis was performed.
Results There were a total of 57 deaths during follow-up. In univariate survival analysis, tumor stage, TLG, entropy from the GLCM and entropy from the Laws’ were predictive for overall survival, but only texture determined by entropy was determined as an independent factor in multivariate analysis (hazard ratio 4.24; 95% confidence interval: 2.54, 7.07; p < .01). There was no statistically significant association between OS and SUVmax, SUVmean, MTV and other texture parameters.
Conclusions In non-small cell lung cancer, texture features determined using 18F-FDG PET images may be significant independent prognostic factors for overall survival. These parameters are potential biomarkers and can provide any additional information over metabolic parameters and clinical staging in routine clinical practice.