立体定向体放疗 (SBRT) 对肝细胞癌 (HCC) 患者产生出色的局部控制效果，但正常肝组织的毒性风险仍然是一个限制因素。已经提出了正常组织并发症概率 (NTCP) 模型以估计毒性，假设肝脏功能分布是均匀的，这不是最优的选择。随着更准确的区域肝脏功能成像可用于个体患者，我们可以提高估计精度并更具个体化。使用术前/放疗期 (RT) 动态氧化钆增强 (DGAE) MRI 开发 NTCP 模型，以患者特异性方式适应 HCC 患者接受 SBRT。146 名接受SBRT治疗的HCC患者中，24 名接受了DGAE MRI检查。将物理剂量转换为EQD2进行分析。使用从DGAE-MRI获取的对比度摄取速率 (k1) 来量化肝功能。使用逻辑剂量-效应模型估计肝功能丧失的比例，并使用 Child-Pugh (C-P) 记分的累积功能储备模型估计NTCP。使用最大似然估计计算模型参数。根据剂量分布和术前 k1 图像预测术中肝脏功能图像，并使用有条件的Wasserstein生成对抗网络 (cWGAN) 评估成像预测质量，使用均方根误差 (RMSE) 和结构相似度 (SSIM) 指标。剂量-效应关系和NTCP都在原始和cWGAN预测图像上进行拟合，并使用Wilcoxon符号秩检验进行比较。变化k1的逻辑剂量反应模型得到整个人群的D50为35.2 (95％CI：26.7-47.5) Gy，k为0.62 (0.49-0.75)。高基线ALBI(肝功能差)亚组D50为11.7 (CI：9.06-15.4) Gy，k为0.96 (CI：0.74-1.22)，明显小于低基线ALBI (肝功能良好) 亚组的54.8 (CI：38.3-79.1) Gy和0.59 (CI：0.48-0.74)，其p值分别为<.001和=.008，这表明肝功能较差的队列具有更高的放射敏感性。还对高/低基线CP亚组进行了子集分析。相应的NTCP模型显示，对于低ALBI亚组，拟合参数在cWGAN预测和现场图像中之间有良好的一致性，没有统计学差异。成功开发了将基于DGAE-MRI k1图像的体素-wise功能信息纳入模型的NTCP模型，并在小患者队列中展示了可行性。cWGAN预测的功能图为评估SBRT局部患者特异性反应提供了希望，并需要更大的患者队列进行进一步验证。本文受版权保护。版权所有。

Stereotactic body radiation therapy (SBRT) produces excellent local control for patients with hepatocellular carcinoma (HCC). However, the risk of toxicity for normal liver tissue is still a limiting factor. Normal tissue complication probability (NTCP) models have been proposed to estimate the toxicity with the assumption of uniform liver function distribution, which is not optimal. With more accurate regional liver functional imaging available for individual patient, we can improve the estimation and be more patient-specific.To develop normal tissue complication probability (NTCP) models using pre-/during-treatment (RT) dynamic Gadoxetic Acid-enhanced (DGAE) MRI for adaptation of RT in a patient-specific manner in hepatocellular cancer (HCC) patients who receive SBRT.24 of 146 HCC patients who received SBRT underwent DGAE MRI. Physical doses were converted into EQD2 for analysis. Voxel-by-voxel quantification of the contrast uptake rate (k1) from DGAE-MRI was used to quantify liver function. A logistic dose-response model was used to estimate the fraction of liver functional loss, and NTCP was estimated using the cumulative functional reserve model for changes in Child-Pugh (C-P) scores. Model parameters were calculated using maximum-likelihood estimations. During-RT liver functional maps were predicted from dose distributions and pre-RT k1 maps with a conditional Wasserstein generative adversarial network (cWGAN). Imaging prediction quality was assessed using root-mean-square error (RMSE) and structural similarity (SSIM) metrics. The dose-response and NTCP were fit on both original and cWGAN predicted images and compared using a Wilcoxon signed-rank test.Logistic dose response models for changes in k1 yielded D50 of 35.2 (95% CI: 26.7-47.5) Gy and k of 0.62 (0.49-0.75) for the whole population. The high baseline ALBI (poor liver function) subgroup showed a significantly smaller D50 of 11.7 (CI: 9.06-15.4) Gy and larger k of 0.96 (CI: 0.74-1.22) compared to a low baseline ALBI (good liver function) subgroup of 54.8 (CI: 38.3-79.1) Gy and 0.59 (CI: 0.48-0.74), with p values of <.001 and = .008, respectively, which indicates higher radiosensitivity for the worse baseline liver function cohort. Subset analyses were also performed for high/low baseline CP subgroups. The corresponding NTCP models showed good agreement for the fit parameters between cWGAN predicted and the ground-truth during-RT images with no statistical differences for low ALBI subgroup.NTCP models which incorporate voxel-wise functional information from DGAE-MRI k1 maps were successfully developed and feasibility was demonstrated in a small patient cohort. cWGAN predicted functional maps show promise for estimating localized patient-specific response to RT and warrant further validation in a larger patient cohort. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.