Introduction: Using multi-isocenter volumetric-modulated arc therapy (VMAT) for total body irradiation (TBI) may improve dose uniformity and vulnerable tissue protection compared with classical whole-body field technique. Two drawbacks limit its application: (1) VMAT-TBI planning is time consuming; (2) VMAT-TBI plans are sensitive to patient positioning uncertainties due to beam matching. This study presents a robust planning technique with image-guided delivery to improve dose delivery accuracy. In addition, a streamlined sim-to-treat workflow with automatic scripts is proposed to reduce planning time.
Materials: Twenty-five patients were included in this study. Patients were scanned in supine head-first and feet-first directions. An automatic workflow was used to (1) create a whole-body CT by registering two CT scans, (2) contour lungs, kidneys, and planning target volume (PTV), (3) divide PTV into multiple sub-targets for planning, and (4) place isocenters. Treatment planning included feathered AP/PA beams for legs/feet and VMAT for the body. VMAT-TBI was evaluated for plan quality, planning/delivery time, and setup accuracy using image guidance.
Results: VMAT-TBI planning time can be reduced to a day with automatic scripts. Treatment time took around an hour per fraction. VMAT-TBI improved dose coverage (PTV V100 increased from 76.8 ± 10.5 to 88.5 ± 2.6; p < 0.001) and reduced lung dose (lung mean dose reduced from 10.8 ± 0.7 Gy to 9.4 ± 0.8 Gy, p < 0.001) compared with classic AP/PA technique.
Conclusion: A VMAT-TBI sim-to-treat workflow with robust planning and image-guided delivery was proposed. VMAT-TBI improved the plan quality compared with classical whole-body field techniques.
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http://dx.doi.org/10.1002/acm2.13412 | DOI Listing |
Clin Transl Radiat Oncol
March 2025
Department of Radiation Oncology, University Hospital Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Purpose: To use imaging data from stereotactic MR-guided online adaptive radiotherapy (SMART) of ultracentral lung tumors (ULT) for development of a safe non-adaptive approach towards stereotactic body radiotherapy (SBRT) of ULT.
Patients And Methods: Analysis is based on 19 patients with ULT who received SMART (10 × 5.0-5.
Radiat Oncol
January 2025
Department of Neurosurgery, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan.
Purpose: In this retrospective study, we aimed to evaluate the efficacy and incidence of radiation-induced brain necrosis (RBN) after volumetric modulated arc therapy-based stereotactic irradiation (VMAT-STI) for brain metastases.
Methods: In the 220 brain metastatic lesions included between January 2020 and June 2022, there were 1-9 concurrently treated lesions (median 1). A biologically effective dose (BED)10 of 80 Gy and a reduced BED10 of 50 Gy were prescribed to the gross tumor volume (GTV) and planning target volume (PTV) (PTV = GTV + 3 mm) margins, respectively.
Adv Radiat Oncol
December 2024
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
Purpose: Breast cancer radiation therapy (RT) techniques have historically delivered mean heart doses (MHDs) in the range of 5 Gy, which have been found to predispose patients to cardiopulmonary toxicities. The purpose of this study was to apply artificial intelligence (AI) cardiac substructure auto-segmentation to evaluate the corresponding substructure doses, whether there are laterality- and technique-specific differences in these doses, and if the doses are significantly associated with cardiorespiratory fitness after state-of-the-art RT planning and delivery for breast cancer.
Methods And Materials: Cardiopulmonary substructures were AI auto-segmented.
Front Oncol
December 2024
Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Background: This study aimed to investigate the effect of couch rotation angles on non-coplanar volumetric modulated arc therapy (ncVMAT) plan for stereotactic body radiotherapy (SBRT) in lung cancer patients and to evaluate the feasibility of clinically applying ncVMAT for SBRT.
Methods: Twenty-four lung cancer patients with a single lesion eligible for SBRT were enrolled in the study. Seven dual partial-arc VMAT plans with varying couch angles were designed for every patient.
PLoS One
January 2025
Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to convert CE-pCT images into pseudo DECT images for model application.
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