Quantitative comparison of pre-treatment predictive and post-treatment measured dosimetry for selective internal radiation therapy using cone-beam CT for tumor and liver perfusion territory definition

Abstract

Background: Selective internal radiation therapy (SIRT) is a promising treatment for unresectable hepatic malignancies. Predictive dose calculation based on a simulation using technetium-99m-labeled macro-aggregated albumin ( 99m Tc-MAA) before the treatment is considered as a potential tool for patient-specific treatment planning. Post-treatment dose measurement is mainly performed to confirm the planned absorbed dose to the tumor and non-tumor liver volumes. This study compared the predicted and measured absorbed dose distributions. Methods: Thirty-one patients (67 tumors) treated by SIRT with resin microspheres were analyzed. Predicted and delivered absorbed dose was calculated using 99m Tc-MAA-SPECT and 90 Y TOF-PET imaging. The voxel-level dose distribution was derived using the local deposition model. Liver perfusion territories and tumors have been delineated on contrast-enhanced CBCT images, which have been acquired during the 99m Tc-MAA work-up. Several dose-volume histogram (DVH) parameters together with the mean dose for liver perfusion territories, non-tumoral and tumoral compartments were evaluated. Results: A strong correlation between the predicted and measured mean dose for non-tumoral volume was observed (r=0.937). The ratio of measured and predicted mean dose to this volume has a first, second, and third quartile range of 0.83, 1.05, and 1.25. The difference between the measured and predicted mean dose did not exceed 11 Gy. The correlation between predicted and measured mean dose to the tumor was moderate (r=0.623) with a mean difference of -9.3 Gy. The ratio of measured and predicted tumor mean dose had a median of 1.01 with the first and third quartile ranges of 0.58 and 1.59, respectively. Our results suggest that 99m Tc-MAA-based dosimetry could predict under or over dosing of the non-tumoral liver parenchyma for almost all cases. For more than two-thirds of the tumors, a predictive absorbed dose correctly indicated either good tumor dose coverage or under-dosing of the tumor. Conclusion: Our results highlight the predictive value of 99m Tc-MAA-based dose estimation to predict non-tumor liver irradiation, which can be applied to prescribe an optimized activity aiming at avoiding liver toxicity. Predictive dosimetry is also moderately reliable to estimate the tumor absorbed dose.