"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy

• Published in: Radiation oncology (London, England) England), 2024-03, Vol.19 (1), p.33-33, Article 33
• Main Authors: Grigo, Johanna, Szkitsak, Juliane, Höfler, Daniel, Fietkau, Rainer, Putz, Florian, Bert, Christoph
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial intelligence; Brain; Brain cancer; Brain tumors; Care and treatment; Computed tomography; Deep learning; Diagnosis; Dosimetry; Feasibility; Feasibility studies; Image quality; Immobilization; Machine learning; Magnetic resonance imaging; Medical imaging; Methods; MRI; MRI-only workflow; Neuroimaging; Patient outcomes; Patient positioning; Patients; Planning; Quality assurance; Radiation dosage; Radiation therapy; Radiotherapy; Registration; sCT; Soft tissues; Synthetic CT; Workflow
• ISSN: 1748-717X; 1748-717X
• DOI: 10.1186/s13014-024-02428-3

Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. 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. Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow. NCT06106997.