Towards artificial intelligence based preoperative dosimetry for liver⁹⁰Y selective internal radiation therapy

Objective.Selective internal radiation therapy delivers Y-90 microspheres through the hepatic arterial system to achieve tumoricidal absorbed dose while limiting normal-tissue irradiation. Current pre-treatment dosimetry requires angiography and 99 mTc-MAA imaging, resulting in an invasive and resource-intensive workflow. This study evaluates the feasibility of estimating post-therapy Y-90 dose distribution directly from diagnostic multiphasic CT using an automated deep-learning pipeline, without requiring angiography or surrogate-particle imaging.Approach.In 152 treatments from the multicenter PROACTIF registry, arterial- and portal-phase CT, together with clinically documented perfusion territories and injected activities, were processed through automated segmentation, rigid registration, differential CT generation, and a generative model synthesizing a PET-like Y-90 activity map. Absorbed-dose maps were computed using the Local Dose Deposition method, and predictive accuracy was assessed using tumor dose-volume metrics and voxelwise spatial agreement.Main results.For the best-performing configuration, the median tumor D70 error was 23 Gy, with substantial inter-patient variability reflecting the challenges of CT-only dose prediction. High-dose coverage (V200) showed a similar pattern. In contrast, spatial agreement was consistently strong, with perfused-volume gamma passing rates around 93% and tumor-level rates around 85%, indicating that the macro-scale distribution of microspheres can be approximated from diagnostic CT. The full pipeline was fully automated and produced predictive dosimetry in under30 S. Significance.Pre-angiography CT contains exploitable information about arterial enhancement and perfusion patterns that enables feasible CT-only prediction of Y-90 spatial distribution. While tumor-level absorbed-dose metrics remain variable, the strong spatial agreement suggests potential utility as an early planning tool to support patient selection or guide catheterization strategy prior to angiography.