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Graph-based risk assessment and error detection in radiation therapy.

Reshma Munbodh1, Juliana K Bowles2, Hitten P Zaveri3

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Medical Physics
|December 19, 2020
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Summary

Automating radiation oncology quality assurance (QA) using graph theory and constraint programming formalizes complex processes. This approach identifies and propagates inconsistencies in cancer treatment plans, enabling automated risk assessment.

Keywords:
automated checksgraphsmodelphysics chart reviewrisk assessment

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Science

Background:

  • Quality assurance (QA) in radiation oncology is a critical, multi-step process for verifying complex cancer treatment plans.
  • Current QA procedures involve interdisciplinary teams and sophisticated, multivendor technology, posing challenges for automation.
  • The pretreatment physics chart review (TPCR) is a key QA step requiring meticulous verification of treatment parameters.

Purpose of the Study:

  • To formalize and automate the quality assurance (QA) process in radiation oncology.
  • To develop a systematic method for identifying and analyzing dependencies and inconsistencies in radiation treatment plans.
  • To apply graph theory and constraint programming to the pretreatment physics chart review (TPCR) for enhanced accuracy and efficiency.

Main Methods:

  • A modular approach decomposed the TPCR into subprocesses, modules, and variables.
  • Directed graphs modeled relationships between variables, while constraint programming formalized error detection as a constraint satisfaction problem.
  • Activity diagrams described the sequence of subprocesses, enabling systematic workflow analysis.

Main Results:

  • A comprehensive model of the TPCR process was developed, including 5 modules, 19 subprocesses, and 346 variables.
  • Inconsistencies were identified through constraint violation checks and graph traversal, revealing their source and propagation.
  • Risk assessment was enabled by combining impact scores from graph traversal with severity scores of inconsistencies.

Conclusions:

  • Directed graphs and constraint programming offer a promising framework for formalizing complex QA in radiation oncology.
  • These methods facilitate automated risk assessment and streamline the pretreatment physics chart review (TPCR) process.
  • Despite its complexity, the formalized QA process is demonstrated to be tractable and automatable.