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Relevant bibliographies by topics / Automation, Planning Optimization, Radiotherapy, Breast

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Journal articles

Academic literature on the topic 'Automation, Planning Optimization, Radiotherapy, Breast'

Author: Grafiati

Published: 9 March 2023

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Journal articles on the topic "Automation, Planning Optimization, Radiotherapy, Breast"

1

Purdie, Thomas G., Robert E. Dinniwell, Daniel Letourneau, Christine Hill, and Michael B. Sharpe. "Automated Planning of Tangential Breast Intensity-Modulated Radiotherapy Using Heuristic Optimization." International Journal of Radiation Oncology*Biology*Physics 81, no. 2 (2011): 575–83. http://dx.doi.org/10.1016/j.ijrobp.2010.11.016.

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2

Fan, J., J. Wang, Z. Zhang, and W. Hu. "EP-1537: Iterative dataset optimization in automated planning: implementation for breast radiotherapy." Radiotherapy and Oncology 123 (May 2017): S826. http://dx.doi.org/10.1016/s0167-8140(17)31972-2.

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Rossi, Maija, Eeva Boman, and Mika Kapanen. "Optimal selection of optimization bolus thickness in planning of VMAT breast radiotherapy treatments." Medical Dosimetry 44, no. 3 (2019): 266–73. http://dx.doi.org/10.1016/j.meddos.2018.10.001.

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4

Fan, Jiawei, Jiazhou Wang, Zhen Zhang, and Weigang Hu. "Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy." Medical Physics 44, no. 6 (2017): 2515–31. http://dx.doi.org/10.1002/mp.12232.

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5

Yin, Chuou, Peng Yang, Shengyuan Zhang, et al. "A self-adaptive prescription dose optimization algorithm for radiotherapy." Open Physics 19, no. 1 (2021): 146–51. http://dx.doi.org/10.1515/phys-2021-0012.

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Abstract Purpose The aim of this study is to investigate an implementation method and the results of a voxel-based self-adaptive prescription dose optimization algorithm for intensity-modulated radiotherapy. Materials and methods The self-adaptive prescription dose optimization algorithm used a quadratic objective function, and the optimization engine was implemented using the molecular dynamics. In the iterative optimization process, the optimization prescription dose changed with the relationship between the initial prescription dose and the calculated dose. If the calculated dose satisfied

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6

Haidar, Ali, Matthew Field, Vikneswary Batumalai, et al. "Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach." Cancers 15, no. 3 (2023): 564. http://dx.doi.org/10.3390/cancers15030564.

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In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A coho

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7

Sayed Omer, Hiba Baha Eldin. "Intensity modulated radiotherapy using Monte Carlo for routine postmastectomy radiotherapy." Polish Journal of Medical Physics And Engineering 18, no. 2 (2012): 49–58. http://dx.doi.org/10.2478/v10013-012-0007-x.

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Radiotherapy given after mastectomy (PMRT) will reduce the risk of local recurrence by about two-thirds. Clinical and dosimetric trials were carried out using various techniques to optimize the treatments by maximizing the dose to the tumour and minimizing it to the healthy tissues at proximity. Different conventional techniques which have been studied suffer from important dose inhomogeneities due to the complex anatomy of the chest, which reduces the benefits from such treatments. Moreover, due to the heterogeneity of breast cancer, the response to therapy and a systematic approach to treatm

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8

Kraus, Kim Melanie, Johanna Winter, Yating Zhang, et al. "Treatment Planning Study for Microbeam Radiotherapy Using Clinical Patient Data." Cancers 14, no. 3 (2022): 685. http://dx.doi.org/10.3390/cancers14030685.

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Microbeam radiotherapy (MRT) is a novel, still preclinical dose delivery technique. MRT has shown reduced normal tissue effects at equal tumor control rates compared to conventional radiotherapy. Treatment planning studies are required to permit clinical application. The aim of this study was to establish a dose comparison between MRT and conventional radiotherapy and to identify suitable clinical scenarios for future applications of MRT. We simulated MRT treatment scenarios for clinical patient data using an inhouse developed planning algorithm based on a hybrid Monte Carlo dose calculation a

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9

Srivastava, Anoop Kumar, Avinav Bharati, Madhup Rastogi, et al. "Evaluation of dosimetric implications of Pareto and constrained mode of optimization for Monaco TPS generated VMAT plans in post operated carcinoma of the left breast." Polish Journal of Medical Physics and Engineering 27, no. 1 (2021): 11–18. http://dx.doi.org/10.2478/pjmpe-2021-0002.

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Abstract Intensity-modulated radiotherapy (IMRT) is being practiced for the last several years with a special approach for radiation therapy in post-mastectomy breast cancer patients. Meeting the cardiac dose constraints has always been a challenge during radiotherapy planning by both IMRT and VMAT (volumetric modulated arc therapy) of post-mastectomy left breast patients. With the advancement in IMRT planning techniques, it has been modified to VMAT with more degrees of freedom for modulation and is being utilised more frequently. This helps in obtaining a suitable plan for achieving both the

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10

Casesnoves, Francisco. "RADIOTHERAPY GENETIC ALGORITHM PARETO-MULTIOBJECTIVE OPTIMIZATION OF BIOLOGICAL EFFECTIVE DOSE AND CLONOGENS MODELS FOR BREAST TUMOR IMPROVED TREATMENT." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (2023): 3102–14. http://dx.doi.org/10.47191/ijmcr/v11i1.02.

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BED model (Biological Effective Dose) for Hyperfractionation TPO was optimized with Pareto-Multiobjective Genetic Algorithms (GA) software. Secondly, the NEffective (Effective Tumor Population Clonogens Number) model optimization for breast cancer clonogens parameters determination in TPO (Treatment Planning Optimization) is carried out with 3D Graphical and Interior Optimization methods. BED model (Biological Effective Dose) for Hyperfractionation TPO was optimized with Pareto-Multiobjective GA software. Results comprise imaging process series and numerical values of NEffective model for brea

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