Zhu Libing, Yu Nathan Y, Tegtmeier Riley C, Ashman Jonathan B, Anand Aman, Duan Jingwei, Chen Quan, Rong Yi
Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85058, USA.
Department of Radiation Oncology, University of South Florida Morsani College of Medicine, Tampa, FL 33606, USA.
Cancers (Basel). 2025 May 3;17(9):1553. doi: 10.3390/cancers17091553.
: 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). : 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). : 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. : 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.
在CT模拟之前识别患者的优势放疗方式具有挑战性。本研究旨在开发一种工作流程，使用深度学习（DL）预测的合成CT（sCT），仅基于诊断CT（dCT）进行治疗方式比较。：利用来自公共数据库的46例胸部病例训练DL网络U-Net，以生成基于最新dCT预测计划CT（pCT）扫描的sCT图像，并在15例机构患者中进行测试。基于平均绝对误差（MAE）和通用质量指数（UQI），将sCT准确性与相应的pCT和商业算法变形CT（MdCT）进行评估。为了确定优势治疗方式，通过一致性相关系数（CCC）在sCT上分析质子和光子治疗计划之间的临床剂量体积直方图（DVH）指标和正常组织并发症概率（NTCP）差异。：在测试病例中，人工智能生成的sCT与商业变形算法的sCT非常相似。sCT与pCT以及MdCT与pCT之间的MAE和UQI值差异分别为19.38 HU和0.06。sCT与pCT之间食管穿孔、肺炎和心包炎的平均绝对NTCP偏差分别为1.54%、0.21%和2.36%。sCT与pCT之间DVH指标的CCC为0.90，NTCP为0.97，表明DVH指标有中度一致性，NTCP有高度一致性。：放射肿瘤学家可以潜在地利用这种基于个性化sCT的方法作为临床支持工具，在患者咨询期间快速比较治疗方式的益处，并促进在患者特定水平上对潜在毒性的深入讨论。
