An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • 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 Date

  • March 15, 2021

Published In

Volume / Issue

  • 109 / 4

Start / End Page

  • 1076 - 1085

PubMed ID

  • 33115686

Pubmed Central ID

  • PMC7902297

Electronic International Standard Serial Number (EISSN)

  • 1879-355X

Digital Object Identifier (DOI)

  • 10.1016/j.ijrobp.2020.10.019

Language

  • eng

Conference Location

  • United States