Background And Purpose: Magnetic resonance (MR)-linac delivery is expected to improve organ at risk (OAR) sparing. In this study, OAR doses were compared for online adaptive MR-linac treatments and conventional cone beam computed tomography (CBCT)-linac radiotherapy, taking into account differences in clinical workflows, especially longer session times for MR-linac delivery.
Materials And Methods: For 25 patients with pelvic/abdominal lymph node oligometastases, OAR doses were calculated for clinical pre-treatment and daily optimized 1.5 T MR-linac treatment plans (5 × 7 Gy) and compared with simulated CBCT-linac plans for the pre-treatment and online anatomical situation. Bowelbag and duodenum were re-contoured on MR-imaging acquired before, during and after each treatment session. OAR hard constraint violations, D and D values were evaluated, focusing on bowelbag and duodenum.
Results: Overall, hard constraints for all OAR were violated less often in daily online MR-linac treatment plans compared with CBCT-linac: in 5% versus 22% of fractions, respectively. D and D values did not differ significantly. When taking treatment duration and intrafraction motion into account, estimated delivered doses to bowelbag and duodenum were lower with CBCT-linac if identical planning target volume (PTV) margins were used for both modalities. When reduced PTV margins were achievable with MR-linac treatment, bowelbag doses were lower compared with CBCT-linac.
Conclusions: Compared with CBCT-linac treatments, the online adaptive MR-linac approach resulted in fewer hard planning constraint violations compared with single-plan CBCT-linac delivery. With respect to other bowelbag/duodenum dose-volume parameters, the longer duration of MR-linac treatment sessions negatively impacts the potential dosimetric benefit of daily adaptive treatment planning.
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http://dx.doi.org/10.1016/j.phro.2022.06.011 | DOI Listing |
J Appl Clin Med Phys
December 2024
Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.
Background: For the development and validation of dynamic treatment modalities and processes on the MR-linac, independent measurements should be performed that validate dose delivery and linac behavior at a high temporal resolution. To achieve this, a detector with both high temporal and spatial resolution is necessary.
Purpose: This study investigates the suitability of a Delta4 Phantom+ MR (Delta4) detector array for time-resolved dosimetry in the 1.
Phys Imaging Radiat Oncol
October 2024
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Purpose: Multiparametric magnetic resonance imaging (MRI) is known to provide predictors for malignancy and treatment outcome. The inclusion of these datasets in workflows for online adaptive planning remains under investigation. We demonstrate the feasibility of longitudinal relaxometry in online MR-guided adaptive stereotactic body radiotherapy (SBRT) to the prostate and dominant intra-prostatic lesion (DIL).
View Article and Find Full Text PDFJ Appl Clin Med Phys
December 2024
Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA.
Purpose: This study assesses a novel, automated dose accumulation process during MR-guided online adaptive radiotherapy (MRgART) for prostate cancer, focusing on inter-fractional anatomical changes and discrepancies between delivered and planned doses.
Methods: A retrospective analysis was conducted on seven prostate cancer patients treated with a five-fraction stereotactic body radiation therapy (SBRT), using a 0.35T MRIdian MR-LINAC system.
J Neurooncol
December 2024
Department of Medicine, University of Toronto, Toronto, ON, Canada.
Purpose: To review applications of cerebral spinal fluid (CSF) biomarkers for the diagnosis, monitoring and treatment of leptomeningeal metastatic disease (LMD) among patients with metastatic solid tumors.
Methods: A narrative review identified original research related to CSF biomarkers among patients with metastatic solid tumors and LMD. Pre-clinical research (e.
Med Phys
December 2024
Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
Background: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.
Purpose: In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatment days.
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