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Targeted α particle therapy (TAT) is ideal for treating disease while minimizing damage to surrounding nontargeted tissues due to short path length and high linear energy transfer (LET). We developed a TAT for metastatic uveal melanoma, targeting the melanocortin-1 receptor (MC1R), which is expressed in 94% of uveal melanomas. Two versions of the therapy are being investigated: 225Ac-DOTA-Ahx-MC1RL (225Ac-Ahx) and 225Ac-DOTA-di-d-Glu-MC1RL (225Ac-di-d-Glu). The biodistribution (BD) from each was studied and a multicompartment pharmacokinetic (PK) model was developed to describe drug distribution rates. Two groups of 16 severe combined immunodeficient (SCID) mice bearing high MC1R expressing tumors were intravenously injected with 225Ac-Ahx or 225Ac-di-d-Glu. After injection, four groups (n= 4) were euthanized at 24, 96, 144, and 288 h time points for each cohort. Tumors and 13 other organs were harvested at each time point. Isomeric γ spectra were measured in tissue samples using a scintillation γ detector and converted to α activity using factors for γ ray abundance per α decay. Time activity curves were calculated for each organ. A five-compartment PK model was built with the following compartments: blood, tumor, normal tissue, kidney, and liver. This model is characterized by a system of five ordinary differential equations using mass action kinetics, which describe uptake, intercompartmental transitions, and clearance rates. The ordinary differential equations were simultaneously solved and fit to experimental data using a genetic algorithm for optimization. The BD data show that both compounds have minimal distribution to organs at risk other than the kidney and liver. The PK parameter estimates had less than 5% error. From these data, 225Ac-Ahx showed larger and faster uptake in the liver. Both compounds had comparable uptake and clearance rates for other compartments. The BD and PK behavior for two targeted radiopharmaceuticals were investigated. The PK model fit the experimental data and provided insight into the kinetics of the compounds systematically.

The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.

Radiomics is being explored for potential applications in radiation therapy. How various imaging protocols affect quantitative image features is currently a highly active area of research. To assess the variability of image features derived from conventional [three-dimensional (3D)] and respiratory-gated (RG) positron emission tomography (PET)/computed tomography (CT) images of lung cancer patients, image features were computed from 23 lung cancer patients. Both protocols for each patient were acquired during the same imaging session. PET tumor volumes were segmented using an adaptive technique which accounted for background. CT tumor volumes were delineated with a commercial segmentation tool. Using RG PET images, the tumor center of mass motion, length, and rotation were calculated. Fifty-six image features were extracted from all images consisting of shape descriptors, first-order features, and second-order texture features. Overall, 26.6% and 26.2% of total features demonstrated less than 5% difference between 3D and RG protocols for CT and PET, respectively. Between 10 RG phases in PET, 53.4% of features demonstrated percent differences less than 5%. The features with least variability for PET were sphericity, spherical disproportion, entropy (first and second order), sum entropy, information measure of correlation 2, Short Run Emphasis (SRE), Long Run Emphasis (LRE), and Run Percentage (RPC); and those for CT were minimum intensity, mean intensity, Root Mean Square (RMS), Short Run Emphasis (SRE), and RPC. Quantitative analysis using a 3D acquisition versus RG acquisition (to reduce the effects of motion) provided notably different image feature values. This study suggests that the variability between 3D and RG features is mainly due to the impact of respiratory motion.