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Formalization and Automation of Quality Assurance Processes in Radiation Oncology

R Munbodh1*, H Zaveri2 , M Caminati3 , J Bowles3 , (1) Warren Alpert Medical School of Brown University, Providence, RI, United States, (2) Yale University School of Medicine, New Haven, CT, United States, (3) University of St Andrews, St Andrews, United Kingdom


(Sunday, 7/29/2018) 3:00 PM - 6:00 PM

Room: Exhibit Hall

Purpose: To formalize and automate quality assurance (QA) in radiation oncology. QA in radiation oncology entails a multi-step verification of complex, high-technology treatments involving an inter-disciplinary team and multi-vendor software and hardware. We addressed the pre-treatment physics chart review (TPCR) using formal methods derived from computer science to dynamically track the solution state and automatically identify logical inconsistencies and how they propagate.

Methods: We used a modular approach to decompose the TPCR process into tractable units comprising sub-processes, modules and variables. Module-associated variables served as inputs to the sub-processes. Modules included "Treatment Planning" and "Record and Verify". Sub-processes included "Dose Prescription", "Documents", "CT Integrity", "Anatomical Contours", "Beam Configuration", "Dose Calculation", "3D Dose Distribution Quality" and "Treatment Approval". Dependencies and conflicts between variables were captured in a dependency matrix. The checks performed and sequence in which sub-processes and variables were visited was described in an activity workflow diagram. The solution state was tracked by assigning states to the variables and sub-processes. We converted the TPCR into a Satisfiability Modulo Theory (SMT) problem and used solvers to detect inconsistencies automatically.

Results: The model for the TPCR process comprised 5 modules, 17 sub-processes and approximately 300 variables (~70 distinct). As proof of concept, we formulated the dependency matrix, activity workflow and checks on a reduced model comprising 11 processes and 70 variables (~30 distinct). We identified 107 dependencies and 1 conflict from the dependency matrix. After using an SMT solver, we identified complete paths through all the variables with a minimal number of dependencies, conflicts and stop-start cycles. We were able to automatically detect variable inconsistencies early and check how changes to one or more variables affected different stages of the process.

Conclusion: Constraint solvers from computer science hold promise for formalizing complex QA processes in radiation oncology and automating the TPCR process.


Quality Assurance, Computer Software, Treatment Verification


IM/TH- Formal quality management tools: General (most aspects)

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