The Statistical Influence of Imaging Time and Segmentation Volume on PET Radiomic Features: A Preclinical Study

Medical imaging plays an essential role in the diagnosis and treatment of many types of cancer. Currently, medical images are assessed visually by radiologists and clinicians. However, the full utility of information contained within medical images has yet to be fully explored. One avenue for this exploration is the utilization of "radiomic features" through the application of texture analysis. The numerous radiomic features proposed may vary with confounding variables such as the time post injection of image acquisition and the accuracy of the delineation of the prescribed segmentation volume. To this avail, we propose using the determinant of the correlation matrix to analyze radiomic features robustness to confounding variables. For this purpose, dynamic pre-clinical positron emission tomography (PET) images of 8 mice with mammary carcinoma xenografts (4T1) were binned into 5 minutes intervals from 50 to 70 minutes post injection. The effect of variation in segmentation was also explored by incrementally increasing segmentation volume. From each image set, we extracted 78 Radiomic features for analysis. Analysis. The statistical association measured by the determinant of the correlation matrix when considering contour size was 0.02378; for acquisition time this value was 0.13296. From this analysis we conclude that both temporal variation and segmentation effect the measurement of temporal features and that texture features are less robust to varying acquisition time than to varying segmentation volume.