Synthetic breath-hold CT generation from free-breathing CT: a novel deep learning approach to predict cardiac dose reduction in deep-inspiration breath-hold radiotherapy
Synthetic breath-hold CT generation from free-breathing CT: a novel deep learning approach to predict cardiac dose reduction in deep-inspiration breath-hold radiotherapy
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DOI：
10.1093/jrr/rrab075
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发表时间：
2021-08-31
影响因子：
2
通讯作者：
Kodaira, Takeshi
中科院分区：
文献类型：
--
作者：
Koide, Yutaro;Shimizu, Hidetoshi;Kodaira, Takeshi
深吸气屏气放疗（DIBH-RT）用于降低心脏放射剂量被广泛使用，但一些患者很少或没有减少。我们构建并比较了两种预测模型，以评估我们的新合成DIBH-CT （sCT）模型的有效性。94例左侧乳腺癌患者（训练组：n = 64，测试组：n = 30）接受了自由呼吸和DIBH计划。开发了基于u - net的sCT生成模型来创建sCT治疗计划。选取以往文献中的解剖学预测因子，构建线性预测模型进行比较。主要预测结果为平均心脏剂量（MHD）降低，计算决定系数（R-2）、均方根误差（RMSE）和平均绝对误差（MAE）。此外，我们还评估了两幅图像之间的心脏和肺部轮廓的相似性和Hounsfield单位（HU）差。DIBH计划的中位MHD减少量为1.14 Gy， sCT计划的中位MHD减少量为1.09 Gy （P = 0.96）。sCT模型的表现优于线性模型（R-2: 0.972 vs 0.450, RMSE: 0.120 vs 0.551, MAE: 0.087 vs 0.412）。dibhct和sCT的器官轮廓相似：心脏的中位Dice （DSC）和Jaccard相似系数（JSC）分别为0.912和0.838，肺的中位Dice （DSC）和Jaccard相似系数（JSC）分别为0.910和0.834。软组织区HU差异小于空气区和骨区HU差异。综上所述，我们的新模型可以通过屏气生成受影响的CT，具有高性能和良好的可视化预测，在放射肿瘤学中可能具有许多潜在的应用前景。
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 (R-2), 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 (R-2: 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.