Background And Purpose: To develop a method of using a multileaf collimator (MLC) to deliver intensity modulated radiotherapy (IMRT) for tangential breast fields, using an MLC to deliver a set of multiple static fields (MSFs).
Materials And Methods: An electronic portal imaging device (EPID) is used to obtain thickness maps of medial and lateral tangential breast fields. From these IMRT deliveries are designed to minimize the volume of breast above 105% of prescribed dose. The deliveries are universally-wedged beams augmented with a set of low dose shaped irradiations. Dosimetric and planning QA of this method has been compared with the standard, wedged treatment and the corresponding treatment using physical compensators. Several options for delivering the MSF treatment are presented.
Results: The MSF technique was found to be superior to the standard technique (P value=0.002) and comparable with the compensated technique. Both IMRT methods reduced the volume of breast above 105% dose from a mean value of 12.0% of the total breast volume to approximately 2.8% of the total breast volume.
Conclusions: This MSF method may be used to reduce the high dose volume in tangential breast irradiation significantly. This may have consequences for long-term side effects, particularly cosmesis.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/s0167-8140(00)00263-2 | DOI Listing |
Med Phys
January 2025
Department of Radiation Oncology, Duke University, North Carolina, USA.
Background: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.
Purpose: To facilitate the clinical treatment planning automation of breast radiation therapy, we utilized reinforcement learning (RL) to develop an auto-planning tool that iteratively edits the fluence maps under the guidance of clinically relevant objectives.
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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!