In Silico Validation of MCID Platform for Monte Carlo-Based Voxel Dosimetry Applied to 90Y-Radioembolization of Liver Malignancies

Featured Application Novel treatment planning system performing Monte Carlo-based voxel dosimetry for a tailored radioembolization of liver malignancies. The aim was the validation of a platform for internal dosimetry, named MCID, based on patient-specific images and direct Monte Carlo (MC) simulations, for radioembolization of liver tumors with Y-90-labeled microspheres. CT of real patients were used to create voxelized phantoms with different density and activity maps. SPECT acquisitions were simulated by the SIMIND MC code. Input macros for the GATE/Geant4 code were generated by MCID, loading coregistered morphological and functional images and performing image segmentation. The dosimetric results obtained from the direct MC simulations and from conventional MIRD approach at both organ and voxel level, in condition of homogeneous tissues, were compared, obtaining differences of about 0.3% and within 3%, respectively, whereas differences increased (up to 14%) introducing tissue heterogeneities in phantoms. Mean absorbed dose for spherical regions of different sizes (10 mm <= r <= 30 mm) from MC code and from OLINDA/EXM were also compared obtaining differences varying in the range 7-69%, which decreased to 2-9% after correcting for partial volume effects (PVEs) from imaging, confirming that differences were mostly due to PVEs, even though a still high difference for the smallest sphere suggested possible source description mismatching. This study validated the MCID platform, which allows the fast implementation of a patient-specific GATE simulation, avoiding complex and time-consuming manual coding. It also points out the relevance of personalized dosimetry, accounting for inhomogeneities, in order to avoid absorbed dose misestimations.

Automated summary

The study validates a novel treatment planning system based on Monte Carlo for personalized radioembolization of liver malignancies. It emphasizes the importance of personalized dosimetry in accurately estimating absorbed dose, especially in the presence of tissue heterogeneities.