Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases
PurposeWhile deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed t...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1285555/full |
| _version_ | 1797510836436074496 |
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| author | Jinghui Pan Jinghui Pan Jinsheng Xiao Changli Ruan Qibin Song Lei Shi Fengjiao Zhuo Hao Jiang Xiangpan Li |
| author_facet | Jinghui Pan Jinghui Pan Jinsheng Xiao Changli Ruan Qibin Song Lei Shi Fengjiao Zhuo Hao Jiang Xiangpan Li |
| author_sort | Jinghui Pan |
| collection | DOAJ |
| description | PurposeWhile deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario.MethodsWe collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny.ResultsDose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%).ConclusionThis study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario. |
| first_indexed | 2024-03-10T05:36:57Z |
| format | Article |
| id | doaj.art-ff7b6bf2f80842ccbb8f0066400f2618 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-10T05:36:57Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-ff7b6bf2f80842ccbb8f0066400f26182023-11-22T22:52:20ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-11-011310.3389/fonc.2023.12855551285555Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastasesJinghui Pan0Jinghui Pan1Jinsheng Xiao2Changli Ruan3Qibin Song4Lei Shi5Fengjiao Zhuo6Hao Jiang7Xiangpan Li8School of Electronic Information, Wuhan University, Wuhan, Hubei, ChinaDepartment of Radiation Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei, ChinaSchool of Electronic Information, Wuhan University, Wuhan, Hubei, ChinaDepartment of Radiation Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei, ChinaDepartment of Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei, ChinaDepartment of Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei, ChinaDepartment of Radiation Oncology, Jiangling County People’s Hospital, Jingzhou, Hubei, ChinaSchool of Electronic Information, Wuhan University, Wuhan, Hubei, ChinaDepartment of Radiation Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei, ChinaPurposeWhile deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario.MethodsWe collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny.ResultsDose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%).ConclusionThis study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario.https://www.frontiersin.org/articles/10.3389/fonc.2023.1285555/fullbrain metastasesstereotactic radiosurgerydose predictiondeep learningradiation oncology |
| spellingShingle | Jinghui Pan Jinghui Pan Jinsheng Xiao Changli Ruan Qibin Song Lei Shi Fengjiao Zhuo Hao Jiang Xiangpan Li Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases Frontiers in Oncology brain metastases stereotactic radiosurgery dose prediction deep learning radiation oncology |
| title | Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases |
| title_full | Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases |
| title_fullStr | Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases |
| title_full_unstemmed | Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases |
| title_short | Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases |
| title_sort | deep learning driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases |
| topic | brain metastases stereotactic radiosurgery dose prediction deep learning radiation oncology |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1285555/full |
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