DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer.
DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer.
复制标题
DAART：用于肺癌深度加速适应性放射治疗的深度学习平台。
DOI：
10.3389/fonc.2023.1201679
复制
发表时间：
2023
影响因子：
4.7
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;Farjam, Reza;Voong, Khinh Ranh;Hales, Russell K.;Du, Yong;Jia, Xun;Ding, Kai
关键词：
该研究旨在实施一种新的深度加速自适应放射治疗（DAART）方法用于肺癌放射治疗（RT）。肺癌是癌症相关死亡的最常见原因，RT是早期非小细胞肺癌（NSCLC）的首选医学不可手术治疗。在目前冗长的工作流程中，从诊断到RT治疗的平均时间为四周，这可能导致完全重新分期和局部控制的延迟。我们实施了DAART方法，采用了一种新型的deepPERFECT系统，以解决诊断和治疗开始之间不必要的延迟。我们开发了一个deepPERFECT，使初始诊断成像适应治疗设置，以允许初始RT计划和验证。我们使用了15例接受RT治疗的NSCLC患者的数据来训练模型并测试其性能。我们进行了一项虚拟临床试验，以评估拟议的DAART用于肺癌放疗的治疗质量。我们发现，deepPERFECT预测计划CT，身体和肺部的平均高强度保真度分别为83和14 HU。与计划CT扫描相比，合成CT上的身体和肺部形状高度适形，骰子相似系数（DSC）分别为0.91和0.97，Hausdorff距离（HD）分别为7.9 mm和4.9 mm。肿瘤显示出较少的适形性，这保证了治疗第1天CT和在线自适应RT的采集。在合成CT上设计初始计划，然后使用适应位置（ATP）和适应形状（ATS）方法适应治疗第1天CT。ATP方案实现了非劣效计划质量，而所有ATS适应计划均显示出良好的计划质量。DAART将常见的在线ART（ART）治疗过程缩短了至少两周，导致治疗时间缩短了50%，以降低复发和局部控制丧失的机会。
The study aimed to implement a novel, deeply accelerated adaptive radiation therapy (DAART) approach for lung cancer radiotherapy (RT). Lung cancer is the most common cause of cancer-related death, and RT is the preferred medically inoperable treatment for early stage non-small cell lung cancer (NSCLC). In the current lengthy workflow, it takes a median of four weeks from diagnosis to RT treatment, which can result in complete restaging and loss of local control with delay. We implemented the DAART approach, featuring a novel deepPERFECT system, to address unwanted delays between diagnosis and treatment initiation. We developed a deepPERFECT to adapt the initial diagnostic imaging to the treatment setup to allow initial RT planning and verification. We used data from 15 patients with NSCLC treated with RT to train the model and test its performance. We conducted a virtual clinical trial to evaluate the treatment quality of the proposed DAART for lung cancer radiotherapy. We found that deepPERFECT predicts planning CT with a mean high-intensity fidelity of 83 and 14 HU for the body and lungs, respectively. The shape of the body and lungs on the synthesized CT was highly conformal, with a dice similarity coefficient (DSC) of 0.91, 0.97, and Hausdorff distance (HD) of 7.9 mm, and 4.9 mm, respectively, compared with the planning CT scan. The tumor showed less conformality, which warrants acquisition of treatment Day1 CT and online adaptive RT. An initial plan was designed on synthesized CT and then adapted to treatment Day1 CT using the adapt to position (ATP) and adapt to shape (ATS) method. Non-inferior plan quality was achieved by the ATP scenario, while all ATS-adapted plans showed good plan quality. DAART reduces the common online ART (ART) treatment course by at least two weeks, resulting in a 50% shorter time to treatment to lower the chance of restaging and loss of local control.
登录
查看更多内容
用于胰腺癌放射治疗的可生物降解水凝胶垫片保留十二指肠的剂量预测模型
DOI：
10.1016/j.ijrobp.2018.07.184
发表时间：
2018-11-01
期刊：
International journal of radiation oncology, biology, physics
影响因子：
--
作者：
Feng Z;Rao AD;Cheng Z;Shin EJ;Moore J;Su L;Kim SH;Wong J;Narang A;Herman JM;McNutt T;Li D;Ding K
通讯作者：
Ding K
影响因子：
5.7
作者：
BARTON, MB;KEANE, TJ;MAKI, E
通讯作者：
MAKI, E
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：
5.7
作者：
Gomez, Daniel R.;Liao, Kai-Ping;Smith, Benjamin D.
通讯作者：
Smith, Benjamin D.
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