Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models.

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Purpose: To develop an automatic treatment planning system for whole breast radiation therapy (WBRT) based on two intensity-modulated tangential fields, enabling near-real-time planning. Methods and Materials: A total of 40 WBRT plans from a single institution were included in this study under IRB approval. Twenty WBRT plans, 10 with single energy (SE, 6MV) and 10 with mixed energy (ME, 6/15MV), were randomly selected as training dataset to develop the methodology for automatic planning. The rest 10 SE cases and 10 ME cases served as validation. The auto-planning process consists of three steps. First, an energy prediction model was developed to automate energy selection. This model establishes an anatomy-energy relationship based on principle component analysis (PCA) of the gray level histograms from training cases' digitally reconstructed radiographs (DRRs). Second, a random forest (RF) model generates an initial fluence map using the selected energies. Third, the balance of overall dose contribution throughout the breast tissue is realized by automatically selecting anchor points and applying centrality correction. The proposed method was tested on the validation dataset. Non-parametric equivalence test was performed for plan quality metrics using one-sided Wilcoxon Signed-Rank test. Results: For validation, the auto-planning system suggested same energy choices as clinical-plans in 19 out of 20 cases. The mean (standard deviation, SD) of percent target volume covered by 100% prescription dose was 82.5% (4.2%) for auto-plans, and 79.3% (4.8%) for clinical-plans (p > 0.999). Mean (SD) volume receiving 105% Rx were 95.2 cc (90.7 cc) for auto-plans and 83.9 cc (87.2 cc) for clinical-plans (p = 0.108). Optimization time for auto-plan was <20 s while clinical manual planning takes between 30 min and 4 h. Conclusions: We developed an automatic treatment planning system that generates WBRT plans with optimal energy selection, clinically comparable plan quality, and significant reduction in planning time, allowing for near-real-time planning.





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Sheng, Yang, Taoran Li, Sua Yoo, Fang-Fang Yin, Rachel Blitzblau, Janet K Horton, Yaorong Ge, Q Jackie Wu, et al. (2019). Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models. Frontiers in Oncology, 9(AUG). p. 750. 10.3389/fonc.2019.00750 Retrieved from

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Yang Sheng

Assistant Professor of Radiation Oncology

My research interest focuses on machine learning and AI application in radiation oncology treatment planning, including prostate cancer, head-and-neck cancer and pancreatic cancer etc.


Sua Yoo

Associate Professor of Radiation Oncology

Patient positioning verification for radiation therapy using OBI/CBCT; Treatment planning for breast cancer radiotherapy;


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


Rachel Catherine Blitzblau

Associate Professor of Radiation Oncology

Janet Knight Horton

Adjunct Associate Professor in the Department of Radiation Oncology

My primary clinical and research focus revolves around women with breast cancer. Specifically, I am interested in studying breast cancer subtype specific (luminal, basal) radiation response in human breast tumors and utilizing that information to design biologically based radiotherapy trials.


Qingrong Wu

Professor of Radiation Oncology

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