This article presents a revised voxel S values (VSVs) approach for dosimetry in targeted radiotherapy, allowing dose calculation for any voxel size and shape of a given SPECT or PET dataset. This approach represents an update to the methodology presented in MIRD pamphlet no. 17. Methods: VSVs were generated in soft tissue with a fine spatial sampling using the Monte Carlo (MC) code MCNPX for particle emissions of 9 radionuclides: 18 F, 90 Y, 99m Tc, 111 In, 123 I, 131 I, 177 Lu, 186 Re, and 201 Tl. A specific resampling algorithm was developed to compute VSVs for desired voxel dimensions. The dose calculation was performed by convolution via a fast Hartley transform. The fine VSVs were calculated for cubic voxels of 0.5 mm for electrons and 1.0 mm for photons. Validation studies were done for 90 Y and 131 I VSV sets by comparing the revised VSV approach to direct MC simulations. The first comparison included 20 spheres with different voxel sizes (3.8-7.7 mm) and radii (4-64 voxels) and the second comparison a hepatic tumor with cubic voxels of 3.8 mm. MC simulations were done with MCNPX for both. The third comparison was performed on 2 clinical patients with the 3D-RD (3-Dimensional Radiobiologic Dosimetry) software using the EGSnrc (Electron Gamma Shower National Research Council Canada)-based MC implementation, assuming a homogeneous tissue-density distribution. Results: For the sphere model study, the mean relative difference in the average absorbed dose was 0.20% 6 0.41% for 90 Y and 20.36% 6 0.51% for 131 I (n 5 20). For the hepatic tumor, the difference in the average absorbed dose to tumor was 0.33% for 90 Y and 20.61% for 131 I and the difference in average absorbed dose to the liver was 0.25% for 90 Y and 21.35% for 131 I. The comparison with the 3D-RD software showed an average voxel-tovoxel dose ratio between 0.991 and 0.996. The calculation time was below 10 s with the VSV approach and 50 and 15 h with 3D-RD for the 2 clinical patients. Conclusion: This new VSV approach enables the calculation of absorbed dose based on a SPECT or PET cumulated activity map, with good agreement with direct MC methods, in a faster and more clinically compatible manner.
In 18 F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from 18 F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations. Methods: PET acquisitions of an anthropomorphic phantom containing 17 spheres (volumes between 0.43 and 97 mL, sphere-to-surrounding-activity concentration ratios between 2 and 68) were used. Forty-one nonspheric tumors (volumes between 0.6 and 92 mL, SUV of 2, 4, and 8) were also simulated and inserted in a real patient 18 F-FDG PET scan. Four threshold-based methods (including one, T bgd , accounting for background activity) and a model-based method (Fit) described in the literature were used for tumor volume measurements. The mean SUV in the resulting volumes were calculated, without and with partial-volume effect (PVE) correction, as well as the maximum SUV (SUV max ). The parameters involved in the tumor segmentation and SUV estimation methods were optimized using 3 approaches, corresponding to getting the best of each method or testing each method in more realistic situations in which the parameters cannot be perfectly optimized. Results: In the phantom and simulated data, the T bgd and Fit methods yielded the most accurate volume estimates, with mean errors of 2% 6 11% and 28% 6 21% in the most realistic situations. Considering the simulated data, all SUV not corrected for PVE had a mean bias between 231% and 246%, much larger than the bias observed with SUV max (211% 6 23%) or with the PVE-corrected SUV based on T bgd and Fit (22% 6 10% and 3% 6 24%). Conclusion: The method used to estimate tumor volume and SUV greatly affects the reliability of the estimates. The T bgd and Fit methods yielded low errors in volume estimates in a broad range of situations. The PVE-corrected SUV based on T bgd and Fit were more accurate and reproducible than SUV max .
This integrated risk model could lead to more accurate selection of patients that would allow better individualization of therapy.
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