deepPERFECT: Novel Deep Learning CT Synthesis Method for Expeditious Pancreatic Cancer Radiotherapy.
deepPERFECT: Novel Deep Learning CT Synthesis Method for Expeditious Pancreatic Cancer Radiotherapy.
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DOI：
10.3390/cancers15113061
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发表时间：
2023-06-05
期刊：
影响因子：
5.2
通讯作者：
Ding, Kai
中科院分区：
文献类型：
--
作者：
Hooshangnejad, Hamed;Chen, Quan;Feng, Xue;Zhang, Rui;Ding, Kai
关键词：
胰腺癌是一种毁灭性疾病，每年新增病例超过 60,000 例，3 年总生存率不到 10%。放射治疗是局部晚期胰腺癌的有效治疗方法。然而，目前的临床 RT 工作流程冗长，并且涉及诊断 CT 和计划 CT 的单独图像采集，这给患者及其护理人员带来了巨大的负担。此外，研究表明快速放射治疗过程可降低死亡率。在这里，我们提出了一种创新的深度学习解决方案，使诊断 CT 中患者的身体形状适应治疗实施设置，从而将治疗开始时间缩短一半。因此，我们的方法还减少了手术时间，并大大降低了胰腺癌进展的风险。标准护理 RT 工作流程延迟的主要原因是需要多次预约和单独的图像采集。在这项工作中，我们解决了如何通过从诊断 CT 合成计划 CT 来加快工作流程的问题。这一想法基于诊断性 CT 可用于 RT 计划的理论，但在实践中，由于患者设置和采集技术的差异，需要单独计划 CT。我们开发了一种生成式深度学习模型 deepPERFECT，经过训练可以捕获这些差异并生成变形向量场，从而将诊断 CT 转换为初步规划 CT。我们从图像质量和剂量学角度进行了详细分析，结果表明，deepPERFECT 使初步 RT 计划能够用于初步和早期计划剂量学评估和评价。
Pancreatic cancer is a devastating disease with more than 60,000 new cases each year and a less than 10 percent 3-year overall survival rate. Radiation therapy is an effective treatment for locally advanced pancreatic cancer. The current clinical RT workflow, however, is lengthy and involves separate image acquisition for diagnostic CT and planning CT, which imposes a huge burden on patients and their caretakers. Moreover, studies have shown a reduction in mortality rate from expeditious radiotherapy treatment courses. Here, we proposed an innovative deep learning solution to adapt the shape of a patient’s body in diagnostic CT to the treatment delivery setup, and consequently, reduce the time to treatment initiation by half. As a result, our method also reduces the time to surgery and greatly decreases the risk of progression for pancreatic cancer. Major sources of delay in the standard of care RT workflow are the need for multiple appointments and separate image acquisition. In this work, we addressed the question of how we can expedite the workflow by synthesizing planning CT from diagnostic CT. This idea is based on the theory that diagnostic CT can be used for RT planning, but in practice, due to the differences in patient setup and acquisition techniques, separate planning CT is required. We developed a generative deep learning model, deepPERFECT, that is trained to capture these differences and generate deformation vector fields to transform diagnostic CT into preliminary planning CT. We performed detailed analysis both from an image quality and a dosimetric point of view, and showed that deepPERFECT enabled the preliminary RT planning to be used for preliminary and early plan dosimetric assessment and evaluation.
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影响因子：
3.8
作者：
Hooshangnejad, Hamed;Youssefian, Sina;Ding, Kai
通讯作者：
Ding, Kai
影响因子：
3.8
作者：
Du, Kaifang;Bayouth, John E.;Reinhardt, Joseph M.
通讯作者：
Reinhardt, Joseph M.
影响因子：
3.8
作者：
Hooshangnejad, Hamed;Han-Oh, Sarah;Shin, Eun Ji;Narang, Amol;Rao, Avani Dholakia;Lee, Junghoon;McNutt, Todd;Hu, Chen;Wong, John;Ding, Kai
通讯作者：
Ding, Kai
影响因子：
4.7
作者：
Hooshangnejad H;Youssefian S;Narang A;Shin EJ;Rao AD;Han-Oh S;McNutt T;Lee J;Hu C;Wong J;Ding K
通讯作者：
Ding K
影响因子：
2.1
作者：
Byrne M;Archibald-Heeren B;Hu Y;Teh A;Beserminji R;Cai E;Liu G;Yates A;Rijken J;Collett N;Aland T
通讯作者：
Aland T