Translating contrast enhanced computed tomography images to liver radioembolization dose distribution for more comprehensively indicating patients

• Published in: Physics in medicine & biology Vol. 69; no. 16; pp. 165016 - 165027
• Main Authors: Mehadji, Brahim, Ruvalcaba, Carlos A, Hernandez, Andrew M, Abdelhafez, Yasser G, Goldman, Roger, Roncali, Emilie
• Format: Journal Article
• Language: English
• Published: IOP Publishing 21.08.2024
• Subjects: dose prediction; liver; radioembolisation; Y-90
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/ad6748

Abstract Objective. Contrast-enhanced computed tomography (CECT) is commonly used in the pre-treatment evaluation of liver Y-90 radioembolization feasibility. CECT provides detailed imaging of the liver and surrounding structures, allowing healthcare providers to assess the size, location, and characteristics of liver tumors prior to the treatment. Here we propose a method for translating CECT images to an expected dose distribution for tumor(s) and normal liver tissue. Approach. A pre-procedure CECT is used to obtain an iodine arterial-phase distribution by subtracting the non-contrast CT from the late arterial phase. The liver segments surrounding the targeted tumor are selected using Couinaud’s method. The resolution of the resulting images is then degraded to match the resolution of the positron emission tomography (PET) images, which can image the Y-90 activity distribution post-treatment. The resulting images are then used in the same way as PET images to compute doses using the local deposition method. CECT images from three patients were used to test this method retrospectively and were compared with Y-90 PET-based dose distributions through dose volume histograms. Main results. Results show a concordance between predicted and delivered Y-90 dose distributions with less than 10% difference in terms of mean dose, for doses greater than 10% of the 98th percentile (D2%). Significance. CECT-derived predictions of Y-90 radioembolization dose distributions seem promising as a supplementary tool for physicians when assessing treatment feasibility. This dosimetry prediction method could provide a more comprehensive pre-treatment evaluation—offering greater insights than a basic assessment of tumor opacification on CT images.