Utility-Based Dose Selection for Stereotactic Body Radiation Therapy in Hepatocellular Carcinoma

Stereotactic body radiation therapy (SBRT) planning for hepatocellular carcinoma (HCC) typically prescribes the maximum dose associated with an acceptable risk of toxicity, relying on implicit trade-offs between efficacy and toxicity. To make this trade-off quantitative, we developed a utility-based approach to dose selection for SBRT in HCC and studied the expected effects on clinical outcomes.

Using a multi-institutional cohort of SBRT-treated patients, we developed predictive models for local progression, competing mortality, and liver toxicity, defined as an increase of 0.5 or more points in albumin-bilirubin score within 6 months. Individualized risks of local progression and toxicity were integrated using a utility framework with a quantitative efficacy/toxicity trade-off. We then performed a simulation comparing predicted clinical outcomes and overall utility between 2 scenarios: if patients in our cohort had been prescribed SBRT according to Radiation Therapy Oncology Group 1112 ("standard dosing"), versus with our proposed utility-based dose selection.

Our cohort included 309 patients (75% Child-Pugh A pretreatment liver function and 24% Child-Pugh B). The median tumor prescription dose in BED₁₀ was 79 Gy₁₀ and the total number of fractions ranged from 2 to 6. The estimated optimal prescription dose under utility-based dose selection varied widely with baseline liver function, with an optimal tumor BED₁₀ of 112 Gy for a patient with Child-Pugh A cirrhosis to 26 Gy for a patient with Child-Pugh C cirrhosis. In the simulation study, the magnitude of utility gains with our approach compared with standard dosing depended on the relative weighting of toxicity and tumor control utilities.

We describe a novel approach to SBRT radiation treatment planning and dose selection for HCC that combines individualized prediction of efficacy, toxicity, and competing mortality with explicit calculation of the efficacy-toxicity trade-off using an expected utility framework. This approach holds promise to personalize SBRT treatment planning.