Goal-Driven Beam Setting Optimization for Whole-Breast Radiation Therapy.

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PURPOSE:To develop an automated optimization program to generate optimal beam settings for whole-breast radiation therapy driven by clinically oriented goals. MATERIALS AND METHODS:Forty patients were retrospectively included in this study. Each patient's planning images, contoured structures of planning target volumes, organs-at-risk, and breast wires were used to optimize for patient-specific-beam settings. Two beam geometries were available tangential beams only and tangential plus supraclavicular beams. Beam parameters included isocenter position, gantry, collimator, couch angles, and multileaf collimator shape. A geometry-based goal function was defined to determine such beam parameters to minimize out-of-field target volume and in-field ipsilateral lung volume. For each geometry, the weighting in the goal function was trained with 10 plans and tested on 10 additional plans. For each query patient, the optimal beam setting was searched for different gantry-isocenter pairs. Optimal fluence maps were generated by an in-house automatic fluence optimization program for target coverage and homogeneous dose distribution, and dose calculation was performed in Eclipse. Automatically generated plans were compared with manually generated plans for target coverage and lung and heart sparing. RESULTS:The program successfully produced a set of beam parameters for every patient. Beam optimization time ranged from 10 to 120 s. The automatic plans had overall comparable plan quality to manually generated plans. For all testing cases, the mean target V95% was 91.0% for the automatic plans and 88.5% for manually generated plans. The mean ipsilateral lung V20Gy was lower for the automatic plans (15.2% vs 17.9%). The heart mean dose, maximum dose of the body, and conformity index were all comparable. CONCLUSION:We developed an automated goal-driven beam setting optimization program for whole-breast radiation therapy. It provides clinically relevant solutions based on previous clinical practice as well as patient specific anatomy on a substantially faster time frame.





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Wang, Wentao, Yang Sheng, Sua Yoo, Rachel C Blitzblau, Fang-Fang Yin and Q Jackie Wu (2019). Goal-Driven Beam Setting Optimization for Whole-Breast Radiation Therapy. Technology in cancer research & treatment, 18. p. 1533033819858661. 10.1177/1533033819858661 Retrieved from https://hdl.handle.net/10161/19367.

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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;


Rachel Catherine Blitzblau

Associate 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


Qingrong Wu

Professor of Radiation Oncology

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