Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT).Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT) based on Mean Absolute Error (MAE) and Universal Quality Index (UQI). To determine advantageous treatment modality, clinical dose-volume histogram (DVH) metrics and Normal Tissue Complication Probability (NTCP) differences between proton and photon treatment plans were analyzed on the sCTs via concordance correlation coefficient (CCC).Results: The AI-generated sCTs closely resembled those of the commercial deformation algorithm in the tested cases. The differences in MAE and UQI values between the sCT-vs-pCT and MdCT-vs-pCT were 19.38 HU and 0.06, respectively. The mean absolute NTCP deviation between sCT and pCT was 1.54%, 0.21%, and 2.36% for esophagus perforation, lung pneumonitis, and heart pericarditis, respectively. The CCC between sCT and pCT was 0.90 for DVH metrics and 0.97 for NTCP, indicating moderate agreement for DVH metrics and substantial agreement.Conclusions: Radiation oncologists can potentially utilize this personalized sCT based approach as a clinical support tool to rapidly compare the treatment modality benefit during patient consultation and facilitate in-depth discussion on potential toxicities at a patient-specific level.
目的：在CT模拟前确定患者的最佳放疗方案具有挑战性。本研究旨在开发一种基于深度学习（DL）预测合成CT（sCT）的工作流程，仅通过诊断CT（dCT）即可进行治疗方案比较。 方法：利用公共数据库中46例胸部病例训练U-Net深度学习网络，根据最新dCT生成预测计划CT（pCT）的sCT图像，并在15例机构患者中进行测试。通过平均绝对误差（MAE）和通用质量指数（UQI）评估sCT与对应pCT及商业形变算法生成CT（MdCT）的准确性。为确定优势治疗方案，通过一致性相关系数（CCC）分析sCT上质子与光子治疗计划的临床剂量体积直方图（DVH）指标及正常组织并发症概率（NTCP）差异。 结果：在测试病例中，AI生成的sCT与商业形变算法结果高度相似。sCT与pCT、MdCT与pCT的MAE和UQI差值分别为19.38 HU和0.06。sCT与pCT的NTCP平均绝对偏差在食管穿孔、放射性肺炎和心包炎方面分别为1.54%、0.21%和2.36%。sCT与pCT的DVH指标CCC为0.90，NTCP为0.97，表明DVH指标具有中等一致性，NTCP具有高度一致性。 结论：放射肿瘤学家可将这种基于个性化sCT的方法作为临床辅助工具，在患者会诊期间快速比较治疗方案获益，并在个体化层面深入探讨潜在毒性风险。
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