ArticleFrontiers in oncology2023
Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Best Practice: CT-guided Adaptive Radiotherapy Reference Planning on the Ethos Platform.Journal of applied clinical medical physics · 2026Review
- Leveraging High-Fidelity Mode for Improved Online Adaptive Stereotactic Accelerated Partial Breast Treatment Efficiency.Advances in radiation oncology · 2025Article
- ADAPT-marketplace - A web-application for XML-based template sharing in radiation oncology.Journal of applied clinical medical physics · 2025Article
- Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.Physics in medicine and biology · 2024Article
- Improved Dosimetry and Plan Quality for Accelerated Partial Breast Irradiation Using Online Adaptive Radiation Therapy: A Single Institutional Study.Advances in radiation oncology · 2024Article
- Evaluation of Ethos intelligent optimization engine for left locally advanced breast cancer.Frontiers in oncology · 2024Article
- Evaluation and correlation of patient movement during online adaptive radiotherapy with CBCT and a surface imaging system.Journal of applied clinical medical physics · 2023Article
- Physicists should perform reference planning for CBCT guided online adaptive radiotherapy.Journal of applied clinical medical physics · 2023Article
- Feasibility study of adaptive radiotherapy with Ethos for breast cancer.Frontiers in oncology · 2023Article
- Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer.Advances in radiation oncologyArticle
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Authors and funding
8 authors.
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Abstract
Background: Accelerated partial breast irradiation (APBI) yields similar rates of recurrence and cosmetic outcomes as compared to whole breast radiation therapy (RT) when patients and treatment techniques are appropriately selected. APBI combined with stereotactic body radiation therapy (SBRT) is a promising technique for precisely delivering high levels of radiation while avoiding uninvolved breast tissue. Here we investigate the feasibility of automatically generating high quality APBI plans in the Ethos adaptive workspace with a specific emphasis on sparing the heart. Methods: Nine patients (10 target volumes) were utilized to iteratively tune an Ethos APBI planning template for automatic plan generation. Twenty patients previously treated on a TrueBeam Edge accelerator were then automatically replanned using this template without manual intervention or reoptimization. The unbiased validation cohort Ethos plans were benchmarked Results: 85% (17/20) of automated validation cohort plans met all planning objectives; three plans did not achieve the contralateral lung V1.5Gy objective, but all other objectives were achieved. Compared to the Eclipse generated plans, the proposed Ethos template generated plans with greater evaluation planning target volume (PTV_Eval) V100% coverage ( Conclusions: Standard left- and right-sided planning templates automatically generated APBI plans of comparable quality to manually generated plans treated on a stereotactic linear accelerator, with a significant reduction in heart dose compared to Eclipse generated plans. The methods presented in this work elucidate an approach for generating automated, cardiac-sparing APBI treatment plans for daily adaptive RT with high efficiency.
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