ArticleFrontiers in oncology2021
Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Comprehensive evaluation of a deep learning-based synthetic CT model for MR-only radiotherapy across multiple anatomical sites.Physics and imaging in radiation oncology · 2026Article
- Clinical validation of using a commercial synthetic-computed tomography solution for brain MRI-only radiotherapy treatment planning.Technical innovations & patient support in radiation oncology · 2025Article
- LUND-PROBE - LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset.Scientific data · 2025Article
- Results of 2023 survey on the use of synthetic computed tomography for magnetic resonance Imaging-only radiotherapy: Current status and future steps.Physics and imaging in radiation oncology · 2024Article
- Review
- "sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.Radiation oncology (London, England) · 2024Article
- Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.Journal of applied clinical medical physics · 2024Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen.Physics and imaging in radiation oncology · 2023Article
- Analytical models for external photon beam radiotherapy out-of-field dose calculation: a scoping review.Frontiers in oncology · 2023Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesMRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup. MATERIAL AND
methodsTwenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed.
resultsTwenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT.
conclusionWe report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.
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