Abstract
(18)F-FDG PET images of tumors often display highly heterogeneous spatial distribution of (18)F-FDG-positive pixels. We proposed that this heterogeneity in (18)F-FDG spatial distribution can be used to predict tumor biologic aggressiveness. This study presents data to support the hypothesis that a new heterogeneity-analysis algorithm applied to (18)F-FDG PET images of tumors in patients is predictive of patient outcome. METHODS: (18)F-FDG PET images from 238 patients with sarcoma were analyzed using a new algorithm for heterogeneity analysis in tumor (18)F-FDG spatial distribution. Patient characteristics, tumor histology, and patient outcome were compared with image analysis results using univariate and multivariate analysis. Cox proportional hazards models were used to further analyze the significance of the data associations. RESULTS: Statistical analyses show that heterogeneity analysis is a strong independent predictor of patient outcome. CONCLUSION: The new (18)F-FDG PET tumor image heterogeneity analysis method is validated for the ability to predict patient outcome in a clinical population of patients with sarcoma. This method can be extended to other PET image datasets in which heterogeneity in tissue uptake of a radiotracer may predict patient outcome.