Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy
Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans by direct prediction of fluence maps from patient anatomy using convolutiona...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-09-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/article/10.3389/frai.2020.00068/full |
| _version_ | 1819019527629832192 |
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| author | Wentao Wang Wentao Wang Yang Sheng Chunhao Wang Jiahan Zhang Xinyi Li Xinyi Li Manisha Palta Brian Czito Christopher G. Willett Qiuwen Wu Qiuwen Wu Yaorong Ge Fang-Fang Yin Fang-Fang Yin Q. Jackie Wu Q. Jackie Wu |
| author_facet | Wentao Wang Wentao Wang Yang Sheng Chunhao Wang Jiahan Zhang Xinyi Li Xinyi Li Manisha Palta Brian Czito Christopher G. Willett Qiuwen Wu Qiuwen Wu Yaorong Ge Fang-Fang Yin Fang-Fang Yin Q. Jackie Wu Q. Jackie Wu |
| author_sort | Wentao Wang |
| collection | DOAJ |
| description | Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans by direct prediction of fluence maps from patient anatomy using convolutional neural networks (CNNs).Materials and Methods: Our proposed framework utilizes two CNNs to predict intensity-modulated radiation therapy fluence maps and generate deliverable plans: (1) Field-dose CNN predicts field-dose distributions in the region of interest using planning images and structure contours; (2) a fluence map CNN predicts the final fluence map per beam using the predicted field dose projected onto the beam's eye view. The predicted fluence maps were subsequently imported into the treatment planning system for leaf sequencing and final dose calculation (model-predicted plans). One hundred patients previously treated with pancreas SBRT were included in this retrospective study, and they were split into 85 training cases and 15 test cases. For each network, 10% of training data were randomly selected for model validation. Nine-beam benchmark plans with standardized target prescription and organ-at-risk constraints were planned by experienced clinical physicists and used as the gold standard to train the model. Model-predicted plans were compared with benchmark plans in terms of dosimetric endpoints, fluence map deliverability, and total monitor units.Results: The average time for fluence-map prediction per patient was 7.1 s. Comparing model-predicted plans with benchmark plans, target mean dose, maximum dose (0.1 cc), and D95% absolute differences in percentages of prescription were 0.1, 3.9, and 2.1%, respectively; organ-at-risk mean dose and maximum dose (0.1 cc) absolute differences were 0.2 and 4.4%, respectively. The predicted plans had fluence map gamma indices (97.69 ± 0.96% vs. 98.14 ± 0.74%) and total monitor units (2,122 ± 281 vs. 2,265 ± 373) that were comparable to the benchmark plans.Conclusions: We develop a novel deep learning framework for pancreas SBRT planning, which predicts a fluence map for each beam and can, therefore, bypass the lengthy inverse optimization process. The proposed framework could potentially change the paradigm of treatment planning by harnessing the power of deep learning to generate clinically deliverable plans in seconds. |
| first_indexed | 2024-12-21T03:36:44Z |
| format | Article |
| id | doaj.art-3bfd1c579102497790433e421eb308f3 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-12-21T03:36:44Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-3bfd1c579102497790433e421eb308f32022-12-21T19:17:19ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122020-09-01310.3389/frai.2020.00068555388Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation TherapyWentao Wang0Wentao Wang1Yang Sheng2Chunhao Wang3Jiahan Zhang4Xinyi Li5Xinyi Li6Manisha Palta7Brian Czito8Christopher G. Willett9Qiuwen Wu10Qiuwen Wu11Yaorong Ge12Fang-Fang Yin13Fang-Fang Yin14Q. Jackie Wu15Q. Jackie Wu16Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesMedical Physics Graduate Program, Duke University, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesMedical Physics Graduate Program, Duke University, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesMedical Physics Graduate Program, Duke University, Durham, NC, United StatesDepartment of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesMedical Physics Graduate Program, Duke University, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesMedical Physics Graduate Program, Duke University, Durham, NC, United StatesPurpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans by direct prediction of fluence maps from patient anatomy using convolutional neural networks (CNNs).Materials and Methods: Our proposed framework utilizes two CNNs to predict intensity-modulated radiation therapy fluence maps and generate deliverable plans: (1) Field-dose CNN predicts field-dose distributions in the region of interest using planning images and structure contours; (2) a fluence map CNN predicts the final fluence map per beam using the predicted field dose projected onto the beam's eye view. The predicted fluence maps were subsequently imported into the treatment planning system for leaf sequencing and final dose calculation (model-predicted plans). One hundred patients previously treated with pancreas SBRT were included in this retrospective study, and they were split into 85 training cases and 15 test cases. For each network, 10% of training data were randomly selected for model validation. Nine-beam benchmark plans with standardized target prescription and organ-at-risk constraints were planned by experienced clinical physicists and used as the gold standard to train the model. Model-predicted plans were compared with benchmark plans in terms of dosimetric endpoints, fluence map deliverability, and total monitor units.Results: The average time for fluence-map prediction per patient was 7.1 s. Comparing model-predicted plans with benchmark plans, target mean dose, maximum dose (0.1 cc), and D95% absolute differences in percentages of prescription were 0.1, 3.9, and 2.1%, respectively; organ-at-risk mean dose and maximum dose (0.1 cc) absolute differences were 0.2 and 4.4%, respectively. The predicted plans had fluence map gamma indices (97.69 ± 0.96% vs. 98.14 ± 0.74%) and total monitor units (2,122 ± 281 vs. 2,265 ± 373) that were comparable to the benchmark plans.Conclusions: We develop a novel deep learning framework for pancreas SBRT planning, which predicts a fluence map for each beam and can, therefore, bypass the lengthy inverse optimization process. The proposed framework could potentially change the paradigm of treatment planning by harnessing the power of deep learning to generate clinically deliverable plans in seconds.https://www.frontiersin.org/article/10.3389/frai.2020.00068/fulldeep learningartificial intelligencefluence maptreatment planningconvolutional neural networkpancreas |
| spellingShingle | Wentao Wang Wentao Wang Yang Sheng Chunhao Wang Jiahan Zhang Xinyi Li Xinyi Li Manisha Palta Brian Czito Christopher G. Willett Qiuwen Wu Qiuwen Wu Yaorong Ge Fang-Fang Yin Fang-Fang Yin Q. Jackie Wu Q. Jackie Wu Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy Frontiers in Artificial Intelligence deep learning artificial intelligence fluence map treatment planning convolutional neural network pancreas |
| title | Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy |
| title_full | Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy |
| title_fullStr | Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy |
| title_full_unstemmed | Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy |
| title_short | Fluence Map Prediction Using Deep Learning Models – Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy |
| title_sort | fluence map prediction using deep learning models direct plan generation for pancreas stereotactic body radiation therapy |
| topic | deep learning artificial intelligence fluence map treatment planning convolutional neural network pancreas |
| url | https://www.frontiersin.org/article/10.3389/frai.2020.00068/full |
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