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An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.

Publication ,  Journal Article
Zhang, J; Wang, C; Sheng, Y; Palta, M; Czito, B; Willett, C; Zhang, J; Jensen, PJ; Yin, F-F; Wu, Q; Ge, Y; Wu, QJ
Published in: Int J Radiat Oncol Biol Phys
March 15, 2021

PURPOSE: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. METHODS AND MATERIALS: The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set. RESULTS: All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process. CONCLUSIONS: We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies.

Duke Scholars

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Published In

Int J Radiat Oncol Biol Phys

DOI

EISSN

1879-355X

Publication Date

March 15, 2021

Volume

109

Issue

4

Start / End Page

1076 / 1085

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Radiotherapy Planning, Computer-Assisted
  • Radiosurgery
  • Pancreatic Neoplasms
  • Oncology & Carcinogenesis
  • Knowledge
  • Humans
  • 5105 Medical and biological physics
  • 3407 Theoretical and computational chemistry
  • 3211 Oncology and carcinogenesis
 

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Zhang, J., Wang, C., Sheng, Y., Palta, M., Czito, B., Willett, C., … Wu, Q. J. (2021). An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys, 109(4), 1076–1085. https://doi.org/10.1016/j.ijrobp.2020.10.019
Zhang, Jiahan, Chunhao Wang, Yang Sheng, Manisha Palta, Brian Czito, Christopher Willett, Jiang Zhang, et al. “An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.Int J Radiat Oncol Biol Phys 109, no. 4 (March 15, 2021): 1076–85. https://doi.org/10.1016/j.ijrobp.2020.10.019.
Zhang J, Wang C, Sheng Y, Palta M, Czito B, Willett C, et al. An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys. 2021 Mar 15;109(4):1076–85.
Zhang, Jiahan, et al. “An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.Int J Radiat Oncol Biol Phys, vol. 109, no. 4, Mar. 2021, pp. 1076–85. Pubmed, doi:10.1016/j.ijrobp.2020.10.019.
Zhang J, Wang C, Sheng Y, Palta M, Czito B, Willett C, Jensen PJ, Yin F-F, Wu Q, Ge Y, Wu QJ. An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys. 2021 Mar 15;109(4):1076–1085.
Journal cover image

Published In

Int J Radiat Oncol Biol Phys

DOI

EISSN

1879-355X

Publication Date

March 15, 2021

Volume

109

Issue

4

Start / End Page

1076 / 1085

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Radiotherapy Planning, Computer-Assisted
  • Radiosurgery
  • Pancreatic Neoplasms
  • Oncology & Carcinogenesis
  • Knowledge
  • Humans
  • 5105 Medical and biological physics
  • 3407 Theoretical and computational chemistry
  • 3211 Oncology and carcinogenesis