Background And Purpose: Deep inspiration breath-hold (DIBH) is a technique that is widely utilised to spare the heart and lungs during breast radiotherapy. In this study, a method was developed to validate directly the intrafraction accuracy of DIBH during breast volumetric modulated arc therapy (VMAT) via internal chest wall (CW) monitoring.
Materials And Methods: In-house software was developed to automatically extract and compare the treatment position of the CW in cine-mode electronic portal image device (EPID) images with the planned CW position in digitally reconstructed radiographs (DRR) for breast VMAT treatments. Feasibility of this method was established by evaluating the percentage of total dose delivered to the target volume when the CW was sufficiently visible for monitoring. Geometric accuracy of the approach was quantified by applying known displacements to an anthropomorphic thorax phantom. The software was used to evaluate (offline) the geometric treatment accuracy for ten patients treated using real-time position management (RPM)-guided DIBH.
Results: The CW could be monitored within the tangential sub-arcs which delivered a median 89% (range 73% to 97%) of the dose to target volume. The phantom measurements showed a geometric accuracy within 1 mm, with visual inspection showing good agreement between the software-derived and user-determined CW positions. For the RPM-guided DIBH treatments, the CW was found to be within ±5 mm of the planned position in 97% of EPID frames in which the CW was visible.
Conclusion: An intrafraction monitoring method with sub-millimetre accuracy was successfully developed to validate target positioning during breast VMAT DIBH.
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http://dx.doi.org/10.1016/j.phro.2023.100419 | DOI Listing |
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.
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.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
October 2024
Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium.
Background And Purpose: With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy.
Materials And Methods: The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS).
Asia Ocean J Nucl Med Biol
January 2025
Nuclear Medicine Unit, National Cancer Institute (NCI), Cairo University, Cairo, Egypt.
Objectives: to investigate the capability of F-fluorodeoxyglucose positron emission tomography/computed tomography ([F]-FDG PET/CT) derived volumetric parameters to predict human epidermal growth factor receptor 2 (HER2) status in breast cancer patients.
Methods: retrospective study enrolled 47 female patients with breast cancer. All patients had pretreatment [F]-FDG PET/CT.
Breast Cancer Res
December 2024
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
Background: Primary luminal breast cancer cells lose their identity rapidly in standard tissue culture, which is problematic for testing hormone interventions and molecular pathways specific to the luminal subtype. Breast cancer organoids are thought to retain tumor characteristics better, but long-term viability of luminal-subtype cases is a persistent challenge. Our goal was to adapt short-term organoids of luminal breast cancer for parallel testing of genetic and pharmacologic perturbations.
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