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

BACKGROUND

Selective Internal Radiation Therapy (SIRT) delivers 90 Y microspheres through the hepatic arterial system to achieve tumoricidal absorbed dose while limiting normal-tissue irradiation. Current pre-treatment dosimetry requires angiography and 99m Tc-MAA imaging, resulting in an invasive and resource-intensive workflow.

PURPOSE: To evaluate the feasibility of estimating post-therapy 90 Y dose distribution directly from diagnostic multiphasic CT using an automated deep-learning pipeline, without requiring angiography or surrogate-particle imaging.

Methods

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 90 Y 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.

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 under 30 seconds.

Conclusion

Pre-angiography CT contains exploitable information about arterial enhancement and perfusion patterns that enables feasible CT-only prediction of 90 Y 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.