Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study
PurposeFast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learn...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.939951/full |
| _version_ | 1797950683579678720 |
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| author | Laura Buchanan Saleh Hamdan Ying Zhang Xinfeng Chen X. Allen Li |
| author_facet | Laura Buchanan Saleh Hamdan Ying Zhang Xinfeng Chen X. Allen Li |
| author_sort | Laura Buchanan |
| collection | DOAJ |
| description | PurposeFast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART.MethodsA conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics.ResultsThe average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU.ConclusionWe have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART. |
| first_indexed | 2024-04-10T22:19:00Z |
| format | Article |
| id | doaj.art-aa4f53f9b872480a9be9ebc754635bd1 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T22:19:00Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-aa4f53f9b872480a9be9ebc754635bd12023-01-18T05:37:29ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-01-011310.3389/fonc.2023.939951939951Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility studyLaura BuchananSaleh HamdanYing ZhangXinfeng ChenX. Allen LiPurposeFast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART.MethodsA conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics.ResultsThe average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU.ConclusionWe have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART.https://www.frontiersin.org/articles/10.3389/fonc.2023.939951/fulladaptive radiation therapyMR-guided adaptive radiation therapyonline replanningreal-time adaptationdeep-learning |
| spellingShingle | Laura Buchanan Saleh Hamdan Ying Zhang Xinfeng Chen X. Allen Li Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study Frontiers in Oncology adaptive radiation therapy MR-guided adaptive radiation therapy online replanning real-time adaptation deep-learning |
| title | Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study |
| title_full | Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study |
| title_fullStr | Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study |
| title_full_unstemmed | Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study |
| title_short | Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study |
| title_sort | deep learning based prediction of deliverable adaptive plans for mr guided adaptive radiotherapy a feasibility study |
| topic | adaptive radiation therapy MR-guided adaptive radiation therapy online replanning real-time adaptation deep-learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.939951/full |
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