Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy.
Abstract
<h4>Purpose</h4>The machine learning-based automated treatment planning (MLAP) tool
has been developed and evaluated for breast radiation therapy planning at our institution.
We implemented MLAP for patient treatment and assessed our clinical experience for
its performance.<h4>Methods and materials</h4>A total of 102 patients of breast or
chest wall treatment plans were prospectively evaluated with institutional review
board approval. A human planner executed MLAP to create an auto-plan via automation
of fluence maps generation. If judged necessary, a planner further fine-tuned the
fluence maps to reach a final plan. Planners recorded the time required for auto-planning
and manual modification. Target (ie, breast or chest wall and nodes) coverage and
dose homogeneity were compared between the auto-plan and final plan.<h4>Results</h4>Cases
without nodes (n = 71) showed negligible (<1%) differences for target coverage and
dose homogeneity between the auto-plan and final plan. Cases with nodes (n = 31) also
showed negligible difference for target coverage. However, mean ± standard deviation
of volume receiving 105% of the prescribed dose and maximum dose were reduced from
43.0% ± 26.3% to 39.4% ± 23.7% and 119.7% ± 9.5% to 114.4% ± 8.8% from auto-plan to
final plan, respectively, all with <i>P</i> ≤ .01 for cases with nodes (n = 31). Mean
± standard deviation time spent for auto-plans and additional fluence modification
for final plans were 12.1 ± 9.3 and 13.1 ± 12.9 minutes, respectively, for cases without
nodes, and 16.4 ± 9.7 and 26.4 ± 16.4 minutes, respectively, for cases with nodes.<h4>Conclusions</h4>The
MLAP tool has been successfully implemented for routine clinical practice and has
significantly improved planning efficiency. Clinical experience indicates that auto-plans
are sufficient for target coverage, but improvement is warranted to reduce high dose
volume for cases with nodal irradiation. This study demonstrates the clinical implementation
of auto-planning for patient treatment and the significant importance of integrating
human experience and feedback to improve MLAP for better clinical translation.
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Journal articlePermalink
https://hdl.handle.net/10161/22468Published Version (Please cite this version)
10.1016/j.adro.2021.100656Publication Info
Yoo, Sua; Sheng, Yang; Blitzblau, Rachel; McDuff, Susan; Champ, Colin; Morrison, Jay;
... Wu, Q Jackie (2021). Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole
Breast Radiation Therapy. Advances in radiation oncology, 6(2). pp. 100656. 10.1016/j.adro.2021.100656. Retrieved from https://hdl.handle.net/10161/22468.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Rachel Catherine Blitzblau
Associate Professor of Radiation Oncology
Colin Eamon Champ
Associate Professor of Radiation Oncology
I am a radiation oncologist interested in researching the interaction between diet,
exercise, and metabolism. My research interests include modulating metabolism through
diet, exercise, general activity, and pharmaceutical agents and the impact of body
composition on outcomes. My goal is to assess whether exercise, and specifically weight
training and functional exercise training can help improve overall health and potentially
cancer-specific outcomes in patients treated for breast cancer and ly
Susan Grams Robison McDuff
Assistant Professor of Radiation Oncology
Qingrong Wu
Professor of Radiation Oncology
Fang-Fang Yin
Gustavo S. Montana Distinguished Professor of Radiation Oncology
Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning
optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy,
image-guided radiation therapy, oncological imaging and informatics
Sua Yoo
Associate Professor of Radiation Oncology
Patient positioning verification for radiation therapy using OBI/CBCT; Treatment planning
for breast cancer radiotherapy;
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