基于深度学习的合成CT用于个性化治疗模式选择,在胸部癌症中选择质子和光子疗法
Libing Zhu1, Nathan Y Yu1, Riley C Tegtmeier1,2
1Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85058, USA.
Cancers
|May 14, 2025
概括
这项研究开发了一种深度学习工作流程,用于从诊断CT (dCT) 来生成合成CT (sCT),以比较放射治疗治疗选择. 人工智能预测的sCT可以进行个性化毒性评估,帮助临床决策.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 人工智能在医学中的应用
背景情况:
- 放射治疗规划需要精确的CT模拟,这是耗时和资源密集的.
- 对于临床医生来说,比较不同的放射疗法 (例如,质子与光子疗法) 预模拟对临床医生来说具有挑战性.
- 深度学习 (DL) 提供了生成合成数据的潜力,以弥合这一差距.
研究的目的:
- 开发和验证基于DL的工作流程,用于从诊断CT (dCT) 扫描生成合成CT (sCT) 图像.
- 与商业可变形图像注册算法相比,评估DL预测的sCTs的准确性.
- 评估sCTs在比较放射治疗治疗方式和预测患者特异性毒性方面的有用性.
主要方法:
- 在46个公共胸部CT数据集上训练U-Net DL模型,从dCT生成sCT.
- 在15个机构患者数据集上测试了DL模型,将sCT精度与计划CT (pCT) 和商业变形CT (MdCT) 相比较.
- 评估指标包括平均绝对误差 (MAE) 和通用质量指数 (UQI). 剂量-体积组图 (DVH) 度量和正常组织并发症概率 (NTCP) 被分析用于治疗方式比较.
主要成果:
- 当与pCT进行评估时,人工智能生成的sCT显示了与商业变形CT (MdCT) 相当的准确性.
- 在sCT-vs-pCT和MdCT-vs-pCT之间,平均绝对误差 (MAE) 和通用质量指数 (UQI) 的差异分别为19.38 HU和0.06.
- 协同相关系数 (CCC) 显示DVH指标的中度一致 (0.90),NTCP的实质一致 (0.97),NTCP绝对平均偏差较低 (1.54%食道穿孔,0.21%肺炎,2.36%心脏周心炎).
结论:
- 开发的DL工作流可以从诊断CT扫描中生成准确的合成CT图像.
- 这种个性化的sCT方法是放射瘤学家的宝贵临床支持工具.
- 它有助于在咨询期间快速比较放射治疗方法和患者特异性毒性讨论.
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