Background And Purpose: Real-time treatment monitoring with the electronic portal imaging device (EPID) can conceptually provide a more accurate assessment of the quality of deep inspiration breath-hold (DIBH) and patient movement during tangential breast radiotherapy (RT). A system was developed to measure two geometrical parameters, the lung depth (LD) and the irradiated width (named here skin distance, SD), along three user-selected lines in MV EPID images of breast tangents. The purpose of this study was to test the system during tangential breast RT with DIBH.
Materials And Methods: Measurements of LDs and SDs were carried out in real time. DIBH was guided with a commercial system using a marker block. Results from 17 patients were assessed. Mean midline LDs,
Results: For 56% (162/288) of the tangents tested,
Conclusions: Real-time treatment monitoring of the internal anatomy during DIBH delivery of tangential breast RT is feasible and useful. The new system requires no additional radiation for the patient.
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http://dx.doi.org/10.1016/j.phro.2022.08.002 | DOI Listing |
Phys Med
January 2025
Medical Physics Dept IRCCS San Raffaele Scientific Institution Milano Italy.
Purpose: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.
Methods: Ten institutions (INST1-INST10, 1481 patients) developed their KB-institutional models for left/right WBI (ten models for right and eight models for left). The transferability of models among centers was assessed based on the overlap of the geometric Principal Component (PC1) of each model when applied to other institutions and/or on the presence of significantly different optimization policies.
Phys Med
December 2024
UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA 5001, Australia; Faculty of Informatics & Science, University of Oradea, Oradea 410087, Romania. Electronic address:
Background: Cardiac substructures are critical organs at risk in left-sided breast cancer radiotherapy being often overlooked during treatment planning. The treatment technique plays an important role in diminishing dose to critical structures. This review aims to analyze the impact of treatment- and patient-related factors on heart substructure dosimetry and to identify the gaps in literature regarding dosimetric reporting of cardiac substructures.
View Article and Find Full Text PDFPhys Med Biol
November 2024
Department of Radiation Oncology, Duke University, Durham, NC, United States of America.
Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and incorporate the analysis to improve the existing AI tool.
View Article and Find Full Text PDFMed Phys
November 2024
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.
Background: The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics.
View Article and Find Full Text PDFOncology
August 2024
Department of Breast and Endocrine Surgery, Osaka International Cancer Institute, Osaka, Japan.
Introduction: In 2018, we reported the results of a study to assess the feasibility of applying the ACOSOG Z0011 criteria to Japanese patients with early-stage breast cancer (median follow-up, 3 years). Their results over the longer term can now be presented. Risk factors for axillary and locoregional recurrence in Z0011-eligible patients are unknown.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!