FDG PET imaging of ESFTs correlates with histologic response to neoadjuvant chemotherapy. SUV2 less than 2.5 is predictive of PFS independent of initial disease stage.

In a retrospective analysis of patients with sarcoma who underwent fluorodeoxyglucose positron emission tomography (FDG PET) imaging, tumor maximum FDG uptake was analyzed for ability to predict patient survival and disease-free interval. Two hundred and nine patients with sarcoma were imaged prior to treatment with neoadjuvant chemotherapy or resection. Tumor FDG uptake expressed as maximum standard uptake value (SUV(max)) was compared with disease-free and overall survival. A multivariate Cox regression analysis was applied to examine the role of SUV(max) in predicting time to death or disease progression, after adjusting for standard clinical prognostic factors. The multivariate analyses showed that the SUV(max) information is a statistically significant independent predictor of patient survival. Tumors with larger SUV(max) have a significantly poorer prognosis. This retrospective analysis indicates that the sarcoma tumor SUV(max) value determined by PET is an independent predictor of survival and disease progression.

We have been exploring techniques for evaluation of fluoro-deoxyglucose (FDG) utilization characteristics in human sarcomas measured with positron emission tomography. In previous work, a measure of spatial heterogeneity based on evaluating the deviation of the FDG utilization distribution within the tumor region from a unimodal elliptically contoured spatial pattern was developed. This measure was shown to be a strong prognostic indicator of time to death. The present work explores a more general measure of heterogeneity which incorporates tumor boundary information. The approach relies on the use of a non-parametric representation for the tumor boundary surface. A set of 179 sarcoma patients with follow-up are evaluated with this technique. The results are analyzed to obtain empirical insight into the factors explaining elliptical heterogeneity. In terms of patient survival, the incorporation of the more sophisticated measure of spatial heterogeneity shows some potential improvement in the prediction risk. Further data will enable us to obtain a clearer empirical understanding of the role of the surface information in the measurement of tumor heterogeneity.