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The goal of this observational study is to show the feasibility of an MRI-only workflow in brain radiotherapy. The main question it aims to answer is:
The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data.
A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| sCT workflow | Other | sCT workflow |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| brain radiotherapy | Radiation | brain radiotherapy planned on synthetic CTs |
|
| Measure | Description | Time Frame |
|---|---|---|
| Proportion of patients that can successfully be treated in an MRI-only workflow | All criteria have to be met for the MRI-only workflow to be classified as successful: Verification criteria that will be assessed:
| 12 month |
| Measure | Description | Time Frame |
|---|---|---|
| Reasons that lead to unfeasibility of an MRI-only workflow | Presence (Yes/No) of individual reasons responsible for the unfeasibility of the MRI-only workflow, as assessed with a predefined checklist. | 12 month |
| Dosimetric differences between MRI-only and standard workflow for radiotherapy treatment planning |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Christoph Bert, Prof. Dr. rer. nat | Contact | ++49(0)9131-85-44213 | christoph.bert@uk-erlangen.de | |
| Florian Putz, PD Dr. med. | Contact | ++49(0)9131-85-33405 | florian.putz@uk-erlangen.de |
| Name | Affiliation | Role |
|---|---|---|
| Christoph Bert, Prof. Dr. rer. nat | Universitätsklinikum Erlangen, Strahlenklinik | Principal Investigator |
| Florian Putz, PD Dr. med. | Universitätsklinikum Erlangen, Strahlenklinik | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Erlangen, Universitätsklinikum Strahlenklinik | Erlangen | 91054 | Germany |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38459584 | Derived | Grigo J, Szkitsak J, Hofler D, Fietkau R, Putz F, Bert C. "sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy. Radiat Oncol. 2024 Mar 8;19(1):33. doi: 10.1186/s13014-024-02428-3. |
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| ID | Term |
|---|---|
| D001932 | Brain Neoplasms |
| ID | Term |
|---|---|
| D016543 | Central Nervous System Neoplasms |
| D009423 | Nervous System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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Paired difference in Dose-volume-histogram parameters (Target coverage, mean, median, near maximum and minimum dose) between MRI-only and standard radiotherapy treatment workflow. |
| 12 month |
| Measurement of intra-MRI patient positional changes | Patient shift (vector magnitude) and rotational errors occuring during the course of MRI acquisition due to patient movement, as assessed by rigid registration of the last acquired MRI sequence in reference to the first sequence. | 12 month |
| Organ at risk contouring accuracy on MRI data | Similarity of organ at risk segmentations defined in MRI compared to reference segmentations defined in CT as assessed by the volumetric and surface Dice score, as well as mean surface and Hausdorff distances. | 12 month |
| D001927 |
| Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |