The purpose of this work is to determine the strength of correlations between imaging data and local tumor grade using spatially specific tumor samples to validate against a histologic gold-standard. This improves our understanding of diagnostic imaging by correlating with underlying biology. Glioma patients were enrolled in an IRB approved prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic (T1, T2, FLAIR, T1 post-contrast, and susceptibility), diffusion tensor, dynamic susceptibility and dynamic contrast sequences. During surgery stereotactic biopsy were collected prior to resection along with image space coordinates of the samples. A random forest were built to predict the grade of each sample using preoperative imaging data. The model was assessed based on classification accuracy, Cohen’s kappa, and sensitivity to higher grade disease Twenty-three patients with fifty-two total biopsy samples were analyzed. The Random Forest method predicted tumor grade at 94% accuracy using four inputs (T2, ADC, CBV and Ktrans). Using conventional imaging only, the overall accuracy decreased (89% overall, κ = 0.78) and 71% of high grade samples were misclassified as lower grade disease. We found that pathologic features can be predicted to high accuracy using clinical imaging data. Advanced imaging data contributed significantly to this accuracy, adding value over accuracies obtained using conventional imaging only. Confirmatory imaging trials are justified.
Multiple studies in the literature have proposed diagnostic thresholds based on Multi-Energy Computed Tomography (MECT) iodine maps. However, it is critical to determine the minimum detectable iodine concentration for MECT systems to assure the clinical accuracy for various measured concentrations for these image types. In this study, seven serial dilutions of iohexol were made with concentrations from 0.03 to 2.0 mg Iodine/mL in 50 mL centrifuge tubes. The dilutions and one blank vial were scanned five times each in two scatter conditions: within a 20.0 cm diameter (Head) phantom, and a 30.0 cm x 40.0 cm elliptical (Body) phantom. This was repeated on a total of six scanners from three vendors: fast-kVp switching, dual-source dual-energy CT, dual-layer detector CT, and split-filter CT. Scan parameters and dose were matched as closely as possible across systems, and iodine maps were reconstructed. Regions-of-Interest (ROIs) were placed on 5 consecutive images within each vial, for a total of 25 measurements per sample. The mean and standard deviation were calculated for each sample. The Limit of Detection (LOD) was defined as the concentration that had a 95% chance of having a signal above the 95% confidence interval of the measured blank samples. The range of LODs was 0.021 – 0.484 mg I/mL in the head phantom and 0.125 – 0.547 mg I/mL in the body phantom. The LOD for iodinated contrast using MECT systems changed with scatter and attenuation conditions. The limit of detection for all conditions was under 0.5 mg Iodine/mL.