Moving towards process-based radiotherapy quality assurance using statistical process control

Area of Science:

• Medical Physics
• Radiotherapy Quality Assurance
• Statistical Process Control

Background:

• Statistical Process Control (SPC) methods are increasingly recommended for radiotherapy quality assurance (QA), particularly for patient-specific QA (PSQA) and proton therapy QA, as highlighted by AAPM Task Group (TG) reports.
• Medical physicists face challenges in selecting appropriate SPC tools and methodologies for effective QA analysis.
• This review consolidates the literature on SPC applications in various radiotherapy QA domains.

Purpose of the Study:

• To summarize the utilization of Statistical Process Control (SPC) methods across different radiotherapy quality assurance (QA) applications.
• To address ambiguities and doubts among medical physicists regarding the selection and application of SPC tools for QA.
• To provide insights into enhancing QA monitoring through appropriate SPC methodology.

Main Methods:

• Literature review of SPC applications in radiotherapy QA, including patient-specific QA (PSQA), routine linear accelerator (Linac) QA, and patient positional verification.
• Analysis of how SPC aids in distinguishing between special and routine sources of variation in QA data.
• Exploration of SPC-based approaches for setting machine-specific, site-specific, and technique-specific Tolerance and Action Limits for PSQA.
• Examination of control chart combinations (Shewhart's and time-weighted) for routine Linac QA.
• Discussion on integrating SPC tools into existing image review modules or developing new clinical software.

Main Results:

• SPC analysis helps differentiate 'special' from 'routine' variations, reducing false positive QA actions.
• A two-stage SPC approach can establish machine-specific, site-specific, and technique-specific limits for improved PSQA monitoring.
• Combining Shewhart's and time-weighted control charts offers enhanced insights for routine Linac QA.
• Implementing SPC tools can significantly improve image review processes in radiotherapy.

Conclusions:

• Effective QA monitoring in radiotherapy relies on the proper selection and understanding of SPC tools, tailored to the available data and process drift.
• SPC methodologies provide a robust framework for enhancing the reliability and efficiency of various radiotherapy QA procedures.
• Adoption of SPC can lead to more accurate QA assessments and optimized treatment delivery.