ArticleJournal of applied clinical medical physics2025
Development and evaluation of deep learning models for estimating the organ at-risk dose constraint from two-dimensional cine magnetic resonance imaging scans during irradiation.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Development and evaluation of deep learning models for estimating the organ at-risk dose constraint from two-dimensional cine magnetic resonance imaging scans during irradiation.Journal of applied clinical medical physics · 2025Article
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10 authors.
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Abstract
purposeTwo-dimensional (2D) cine magnetic resonance imaging (MRI), available with a MR-linear accelerator (MR-Linac), allows real-time visualization of anatomical information during irradiation. The present study aimed to develop and evaluate a deep learning model that can estimate the organ-at-risk (OAR) dose constraints (mainly bladder V37Gy) from 2D cine MRI.
methodsThe present study enrolled 91 prostate cancer patients treated with MR-Linac. From 381 treatment fractions, sagittal images at the start and end of the 2D cine MRI were extracted. Additionally, 3D MRI data acquired pre- and post-irradiation were collected, from which bladder V37Gy was calculated. We designed the deep learning model to predict the end-of-irradiation bladder V37Gy value based on the bladder image on the end-of-irradiation 2D cine MRI. The model inputs included the start and end 2D cine MR images, a difference image between them, and the pre-irradiation bladder V37Gy. The model output was the post-irradiation bladder V37Gy. We utilized a five-fold cross-validation for model training and evaluated the performance using a test dataset. For reference, we also evaluated the predictions made using only the pre-irradiation bladder V37Gy.
resultsIn the test dataset, the model-predicted and true bladder V37Gy values showed a strong correlation (r = 0.89), with a mean absolute error (MAE) of 1.40 cm
conclusionsOur results demonstrated that deep learning can effectively predict the OAR dose constraints during irradiation. However, it is noteworthy that these results show only a limited improvement and are constrained by several limitations.
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