Analysis and prediction of liver volume change maps derived from computational tomography scans acquired pre- and post-radiation therapy.
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Abstract
External beam radiation therapy (EBRT) of liver cancers can cause local liver atrophy as a result of tissue damage or hypertrophy as a result of liver regeneration. Predicting those volumetric changes would enable new strategies for liver function preservation during treatment planning. However, understanding of the spatial dose/volume relationship is still limited. This study leverages the use of deep learning-based segmentation and biomechanical deformable image registration (DIR) to analyze and predict this relationship. Pre- and Post-EBRT imaging data were collected for 100 patients treated for HCC, CC or CRC with IMRT with prescription doses ranging from 50 to 100 Gy delivered in 10 to 28 fractions. For each patient, DIR between the portal and venous (PV) phase of a diagnostic CT scan acquired before RT planning, and a PV phase of a diagnostic CT scan acquired after the end of RT (on average 147±36 days) was performed to calculate Jacobian maps representing volume changes in the liver. These volume change maps were used: (i): to analyze the dose/volume relationship in the whole liver and individual Couinaud’s segments; and (ii): to investigate the use of deep-learning to predict a Jacobian map solely based on the pre-RT diagnostic CT and planned dose distribution. Moderate correlations between mean equivalent dose in 2Gy fractions (EQD2) and volume change was observed for all liver sub-regions analyzed individually with Pearson correlation r ranging from -0.36 to -067. The predicted volume change maps showed a significantly stronger voxel-wise correlation with the DIR-based volume change maps than when considering the original EQD2 distribution (0.63±0.24 vs 0.55±23, respectively), demonstrating the ability of the proposed approach to establish complex relationships between planned dose and liver volume response months after treatment, which represents a promising prediction tool for the development of future adaptive and personalized liver radiation therapy strategies.Creative Commons Attribution license.