FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort

Abstract

Intra-tumor uptake heterogeneity in ¹⁸F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural features (TF) analysis is a promising method for its quantification. An open issue associated with the use of TF for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown as a significant predictive and prognostic parameter. Our objective was to address this question using a larger cohort of patients covering different cancer types. Methods: A single database of 555 pre-treatment ¹⁸F-FDG PET images (breast, cervix, esophageal, head & neck and lung cancer tumors) was assembled. Four robust and reproducible TF-derived parameters were considered. The issues associated with the calculation of TF using co-occurrence matrices (such as the quantization and spatial directionality relationships) were also investigated. The relationship between these features and MATV, as well as among the features themselves was investigated using Spearman rank coefficients, for different volume ranges. The complementary prognostic value of MATV and TF was assessed through multivariate Cox analysis in the esophageal and NSCLC cohorts. Results: A large range of MATVs was included in the population considered (3-415 cm³, mean = 35, median = 19, SD=50). The correlation between MATV and TF varied greatly depending on the MATVs, with reduced correlation for increasing volumes. These findings were reproducible across the different cancer types. The quantization and the calculation method both had an impact on the correlation. Volume and heterogeneity were independent prognostic factors (P = 0.0053 and 0.0093 respectively) along with stage (P = 0.002) in NSCLC, but in the esophageal tumors, volume and heterogeneity had less complementary value due to smaller overall volumes. Conclusion: Our results suggest that heterogeneity quantification and volume may provide valuable complementary information for volumes above 10cm³, although the complementary information increases substantially with larger volumes.