Literature DB >> 31956001

Optimization of autogenerated chest-wall radiation treatment plans developed for postmastectomy breast cancer patients in underserved clinics.

Temiloluwa O Esho1, Christine V Chung1, Juanita U Thompson1, Mahsa Dehghanpour1, Jordan R Sutton1, Simona F Shaitelman1, Kelly K Kisling1, Laurence E Court2.   

Abstract

Over the past decade, several strides have been made to improve the management of breast cancer in developing countries; however, there are still obstacles present. In the area of radiation therapy, these hurdles include limited access to radiotherapy treatment and scarcity of oncology specialists. In an effort to reduce inequities in cancer care while improving patient outcomes, our research is focused on developing automated postmastectomy radiation therapy (PMRT) plans for breast cancer patients in these underserved communities that can be further improved upon through treatment planning system (TPS) specific optimization guidelines. The automated planning tool utilized algorithms integrated with Varian's Eclipse TPS. The tool created PMRT plans that used monoisocentric tangents and supraclavicular (SCV) fields with a mix of high and low energy photon beams along with field-in-field (FIF) segments. The completed autogenerated PMRT plans were imported into Phillip's Pinnacle 9.10 and Varian's Eclipse 13.6 TPSs to be further improved through manual optimization; the time required to complete this step was measured and assessed. A senior dosimetrist, physicist, and physician evaluated the optimized plans for clinical acceptability. Guidelines were developed for the planning systems that can be implemented by personnel with either limited experience in radiation treatment planning or those with limited time to produce treatment plans. The autogenerated plans in conjunction with our guidelines have shown to significantly reduce the time required to produce a clinically acceptable PMRT plan from approximately 120 ± 60 minutes to just 13 ± 11 (Pinnacle) and 12 ± 7 (Eclipse) minutes, reducing the total uninterrupted treatment planning time by an average of 108 ± 51 minutes. The results from this research indicate that the autogenerated PMRT plans along with the optimization guidelines are a viable option to provide quality and clinically acceptable PMRT plans that are more efficient and consistent for postmastectomy breast cancer patients in severely underserved communities.
Copyright © 2020. Published by Elsevier Inc.

Entities:  

Keywords:  Auto-planning; LMICs; Optimization guidelines; Post mastectomy radiation therapy

Mesh:

Year:  2020        PMID: 31956001     DOI: 10.1016/j.meddos.2019.12.003

Source DB:  PubMed          Journal:  Med Dosim        ISSN: 1873-4022            Impact factor:   1.482


  2 in total

1.  Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy.

Authors:  Sua Yoo; Yang Sheng; Rachel Blitzblau; Susan McDuff; Colin Champ; Jay Morrison; Leigh O'Neill; Suzanne Catalano; Fang-Fang Yin; Q Jackie Wu
Journal:  Adv Radiat Oncol       Date:  2021-01-22

2.  Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors.

Authors:  Raphael J Douglas; Adenike Olanrewaju; Lifei Zhang; Beth M Beadle; Laurence E Court
Journal:  J Appl Clin Med Phys       Date:  2022-07-05       Impact factor: 2.243

  2 in total

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