Deep-inspiration breath-hold radiotherapy (DIBH-RT) to reduce the cardiac dose irradiation is widely used but some patients experience little or no reduction. We constructed and compared two prediction models to evaluate the usefulness of our new synthetic DIBH-CT (sCT) model. Ninety-four left-sided breast cancer patients (training cohort: n = 64, test cohort: n = 30) underwent both free-breathing and DIBH planning. The U-Net-based sCT generation model was developed to create the sCT treatment plan. A linear prediction model was constructed for comparison by selecting anatomical predictors of past literature. The primary prediction outcome is the mean heart dose (MHD) reduction, and the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) were calculated. Moreover, we evaluated the heart and lungs contours' similarity and Hounsfield unit (HU) difference between both images. The median MHD reduction was 1.14 Gy in DIBH plans and 1.09 Gy in sCT plans (P = 0.96). The sCT model achieved better performance than the linear model (R2: 0.972 vs 0.450, RMSE: 0.120 vs 0.551, MAE: 0.087 vs 0.412). The organ contours were similar between DIBH-CT and sCT: the median Dice (DSC) and Jaccard similarity coefficients (JSC) were 0.912 and 0.838 for the heart and 0.910 and 0.834 for the lungs. The HU difference in the soft-tissue region was smaller than in the air or bone. In conclusion, our new model can generate the affected CT by breath-holding, resulting in high performance and well-visualized prediction, which may have many potential uses in radiation oncology.
Keywords: breast cancer; deep learning; deep-inspiration breath-hold (DIBH); machine learning (ML); predictive assay; radiotherapy.
© The Author(s) 2021. Published by Oxford University Press on behalf of The Japanese Radiation Research Society and Japanese Society for Radiation Oncology.