Abstract
Identification of FDGavid- neoplasms may be obscured by high-uptake normal tissues, thus limiting inferences about the natural history of disease. We introduce a FDG-PET radiomics tissue classifier for differentiating FDGavid- normal tissues from tumor. Thirty-three scans from 15 patients with Hodgkin lymphoma and 68 scans from 23 patients with Ewing sarcoma treated on two prospective clinical trials were retrospectively analyzed. Disease volumes were manually segmented on FDG-PET and CT scans. Brain, heart, kidneys and bladder and tumor volumes were automatically segmented on PET images. Standard-uptake-value (SUV) derived shape and first order radiomics features were computed to build a random forest classifier. Manually segmented volumes were compared to automatically segmented tumor volumes. Classifier accuracy for normal tissues was 90%. Classifier performance was varied across normal tissue types (brain, left kidney and bladder, hear and right kidney were 100%, 96%, 97%, 83% and 87% respectively). Automatically segmented tumor volumes showed high concordance with the manually segmented tumor volumes (R2 = 0.97). Inclusion of texture-based radiomics features minimally contributed to classifier performance. Accurate normal tissue segmentation and classification facilitates accurate identification of FDGavid tissues and classification of those tissues as either tumor or normal tissue.
Highlights
Radiomic feature analysis is a valuable means of evaluating information from FDGavid- tissues[11,12,13,14,15]
Among the FDG-avid normal tissues, brain and bladder were present in all Hodgkin disease (HOD) and Ewing sarcoma (EWS) scans
Automatic segmentation performance showed a median and 95% confidence interval (CI) of 0.72, 0.99, 0.78, 0.99, 0.7 and 0.52 for sensitivity, specificity, precision, accuracy, dice similarity coefficient and Jaccard index respectively
Summary
Radiomic feature analysis is a valuable means of evaluating information from FDGavid- tissues[11,12,13,14,15]. Challenges in PET segmentation include image resolution, variability in shape and location of pathologies and image noise[16]. Despite the challenges with auto-segmentation of FDG-avid tissues, quantitative radiomics features have been successfully correlated with disease prognosis and classification[17,18,19,20]. We retrospectively evaluate the ability of radiomics features derived from SUV and shape data to differentiate FDG-avid normal tissues from tumor tissue using a cohort of Hodgkin lymphoma and Ewing sarcoma patients treated consecutively on two prospective clinical trials. We investigate additional avenues for improvement to the presented method which have the potential to increase the throughput, accuracy and precision of analysis of FDG-PET studies from clinical trials
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