Purpose: To identify disease-specific profiles comprising patient characteristics and imaging biomarkers on contrast-enhanced (CE)-computed tomography (CT) that enable the non-invasive prediction of the hepatopulmonary shunt fraction (HPSF) in patients with hepatocellular carcinoma (HCC) before resin-based transarterial radioembolization (TARE).
Patients and methods: This institutional review board-approved (EA2/071/19) retrospective study included 56 patients with HCC recommended for TARE. All patients received tri-phasic CE-CT within 6 weeks prior to an angiographic TARE evaluation study using technetium-99m macroaggregated albumin. Imaging biomarkers representative of tumor extent, morphology, and perfusion, as well as disease-specific clinical parameters, were used to perform data-driven variable selection with backward elimination to generate multivariable linear regression models predictive of HPSF. Results were used to create clinically applicable risk scores for patients scheduled for TARE. Additionally, Cox regression was used to identify independent risk factors for poor overall survival (OS).
Results: Mean HPSF was 13.11% ± 7.6% (range: 2.8- 35.97%). Index tumor diameter (p = 0.014) or volume (p = 0.034) in combination with index tumor non-rim arterial phase enhancement (APHE) (p < 0.001) and washout (p < 0.001) were identified as significant non-invasive predictors of HPSF on CE-CT. Specifically, the prediction models revealed that the HPSF increased with index lesion diameter or volume and showed higher HPSF if non-rim APHE was present. In contrast, index tumor washout was associated with decreased HPSF levels. Independent risk factors of poorer OS were radiogenomic venous invasion and ascites at baseline.
Conclusion: The featured prediction models can be used for the initial non-invasive estimation of HPSF in patients with HCC before TARE to assist in clinical treatment evaluation while potentially sparing ineligible patients from the angiographic shunt evaluation study.
Keywords: HCC; SIRT; TARE; contrast-enhanced computed tomography; liver cancer.
© 2023 Hamm et al.