DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer
PurposeThe 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 canc...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1201679/full |
| _version_ | 1797785681837162496 |
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| author | Hamed Hooshangnejad Hamed Hooshangnejad Hamed Hooshangnejad Quan Chen Xue Feng Rui Zhang Reza Farjam Khinh Ranh Voong Russell K. Hales Yong Du Xun Jia Kai Ding Kai Ding |
| author_facet | Hamed Hooshangnejad Hamed Hooshangnejad Hamed Hooshangnejad Quan Chen Xue Feng Rui Zhang Reza Farjam Khinh Ranh Voong Russell K. Hales Yong Du Xun Jia Kai Ding Kai Ding |
| author_sort | Hamed Hooshangnejad |
| collection | DOAJ |
| description | PurposeThe 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.Materials and methodsWe 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.ResultsWe 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.ConclusionDAART 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. |
| first_indexed | 2024-03-13T00:57:26Z |
| format | Article |
| id | doaj.art-ca7a4e9c43704007a9dc9bed85e42d03 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-13T00:57:26Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-ca7a4e9c43704007a9dc9bed85e42d032023-07-06T18:16:36ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-07-011310.3389/fonc.2023.12016791201679DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancerHamed Hooshangnejad0Hamed Hooshangnejad1Hamed Hooshangnejad2Quan Chen3Xue Feng4Rui Zhang5Reza Farjam6Khinh Ranh Voong7Russell K. Hales8Yong Du9Xun Jia10Kai Ding11Kai Ding12Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United StatesCarnegie Center of Surgical Innovation, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA, United StatesCarina Medical, Lexington, KY, United StatesDivision of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, United StatesDepartment of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Radiology and Radiological Science, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United StatesDepartment of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United StatesCarnegie Center of Surgical Innovation, Johns Hopkins School of Medicine, Baltimore, MD, United StatesPurposeThe 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.Materials and methodsWe 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.ResultsWe 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.ConclusionDAART 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1201679/fulldeep learningmachine learningimage synthesisadaptive radiation therapy (ART)artificial intelligencenon small cell lung cancer (NSCLC) |
| spellingShingle | Hamed Hooshangnejad Hamed Hooshangnejad Hamed Hooshangnejad Quan Chen Xue Feng Rui Zhang Reza Farjam Khinh Ranh Voong Russell K. Hales Yong Du Xun Jia Kai Ding Kai Ding DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer Frontiers in Oncology deep learning machine learning image synthesis adaptive radiation therapy (ART) artificial intelligence non small cell lung cancer (NSCLC) |
| title | DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer |
| title_full | DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer |
| title_fullStr | DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer |
| title_full_unstemmed | DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer |
| title_short | DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer |
| title_sort | daart a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer |
| topic | deep learning machine learning image synthesis adaptive radiation therapy (ART) artificial intelligence non small cell lung cancer (NSCLC) |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1201679/full |
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