Assessment and Management of Interfraction Variations of Lumpectomy Cavities in Accelerated Partial Breast Irradiation
Therapeutic Radiology and Oncology(2019)
Abstract
Background: The purpose of this study is to quantitatively characterize interfraction variations of lumpectomy cavity (LC) in accelerated partial breast irradiation (APBI) and their dosimetric impacts, and to explore the use of an online adaptive replanning scheme to address these variations. Methods: A total of about 100 diagnostic-quality CT sets acquired using an in-room CT at each fraction during image-guided radiation therapy (IGRT) for ten randomly-selected patients treated with APBI in the supine position were analyzed. The LC, treated breast, lung and heart were delineated on each fraction CT. Organ volume change and deformation were quantified. For each fraction CT, three types of plans were created: adaptive, repositioning, and fully re-optimized plans. The plan qualities were compared. Results: Significant changes in LC shape and volume were observed during APBI. On average, the LC volume decreases by 23% from the planning CT. The average change in LC shape, as measured by the Dice’s coefficient, is 80%. For all patients, the adaptive plans were comparable to the re-optimization plans. For small and moderate LC changes (70%), the three types of plans were comparable, indicating that the current IGRT with the standard margins was sufficient to account for the interfraction variations. For cases with extreme LC change (30%), the adaptive plans offered improved target coverage and/or normal tissue sparing as compared with the repositioning plans. Conclusions: Significant variations in the LC between planning and treatment were found for APBI. The current practice of IGRT with standard planning target volume margins can account for these variations for most cases. Online adaptive replanning was needed for cases with extremely large changes in LC.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined