Abstract
PURPOSE: [(18)F] fluoroestradiol (FES) is an FDA-approved tracer that measures functional estrogen receptor (ER) expression and can estimate the likelihood of response to ER-targeted therapy. In this exploratory analysis, we tested a novel radiomics based analysis of dynamic volumetric FES PET images to predict outcomes in patients with metastatic ER positive breast cancer treated with endocrine therapy. METHODS: We utilized the Rad-Fit method, previously tested in an FDG PET data set, to identify and characterize intratumor subregions of heterogeneous time-activity through an unsupervised clustering approach. A scaled silhouette score was implemented to determine the optimal number of intratumor subregions on a per-tumor basis. Summary statistics of sum of squared error (SSE) and distance between sub regions as well as the total number of intratumor subregions were used to build prognostic models of overall survival (OS) and progression free survival (PFS). We employed Kaplan-Meyer analysis to determine model performance. RESULTS: The radiomic phenotype differentiated between a high and low risk group for progression free survival (C = 0.67, p = 0.025) in the single tumor scenario. Radiomic features of subregion distance classified a high and low risk group for OS in a single tumor (C = 0.67, p = 0.008) and average tumor (C = 0.65, p = 0.017) scenario. CONCLUSIONS: In this exploratory study, 4D radiomic features extracted from dynamic FES PET images can improve the prediction of outcomes in metastatic ER positive breast cancer. Metrics of tumor subregion distance and radiomic phenotype appear to perform as the best radiomic predictors for risk stratification of OS and PFS respectively by potentially reflecting characteristics of the overall tumor heterogeneity in FES PET images. CLINICAL TRIAL NUMBER: not applicable.