Patient-specific modeling enables specific activity optimization to maximize tumor absorbed dose in transarterial radioembolization

Transarterial radioembolization (TARE) is evolving from palliative to curative in the management of hepatocellular carcinoma (HCC). Alongside, there is a growing need to develop personalized and predictive dosimetry strategies to maximize the therapeutic effect. The objective of this study was to evaluate the feasibility of a novel framework, MIDOS, to design patient-specific optimal TARE plans. A retrospective cohort of fourteen TARE patients with solitary HCC who had [ 99m Tc]Tc-MAA SPECT-CT was analyzed, including segmental and lobar administrations. For each patient, tumor and perfused area segmentations were included in a liver vasculature model. In our simulations, microspheres were stochastically assigned in cluster patterns to tumor or normal liver tissue, with probabilities proportional to flow and modulated by tumor and normal liver uptake models. Simulation results were employed to generate synthetic [ 99m Tc]Tc-MAA SPECT images that were compared with patient images using the relative activity errors in tumor and normal tissue, tumor-to-normal ratio (TNR), and gamma analysis (10%/10 mm) of normal tissue distribution. Virtual 90 Y treatment plans were generated using the prescribed activity across a range of specific activities (40–4000 Bq/bead), from which personalized treatment planning curves for TNR and tumor and normal liver mean absorbed dose were derived. Synthetic [ 99m Tc]Tc-MAA SPECT images presented relative errors below 2% for overall tumor activity, normal liver activity, and TNR. Pass rates of 10%/10 mm for normal tissue distribution were above 90% in all patients. Treatment planning curves revealed patient- and scenario-specific optima in TNR, corresponding to maximal tumor mean dose and minimal normal liver mean dose, with substantial inter-patient variability in dose sensitivity to specific activity. This study shows the feasibility of MIDOS as a framework for quantitative treatment optimization in TARE under different clinical scenarios. This allows systematic optimization of treatment planning curves that maximize TNR and tumor mean dose as a function of the 90 Y specific activity.