Bibliographic record
Abstract
Introduction Treatment planning systems (TPS) are a cornerstone of modern radiation therapy. Errors in their commissioning or use can have a devastating impact on many patients. To support safe and high quality care, medical physicists must conduct efficient and proper commissioning, good clinical integration, and ongoing quality assurance (QA) of the TPS. AAPM Task Group 53 and related publications have served as seminal benchmarks for TPS commissioning and QA over the past two decades. Over the same time, continuing innovations have made the TPS even more complex and more central to the clinical process. Medical goals are now expressed in terms of the dose and margins around organs and tissues that are delineated from multiple imaging modalities (CT, MR and PET); and even temporally resolved (i.e., 4D) imaging. This information is passed on to optimization algorithms to establish accelerator movements that are programmed directly for IMRT, VMAT and stereotactic treatments. These advances have made commissioning and QA of the TPS much more challenging. This education session reviews up‐to‐date experience and guidance on this subject; including the recently published AAPM Medical Physics Practice Guideline (MPPG) #5 “Commissioning and QA of Treatment Planning Dose Calculations: Megavoltage Photon and Electron Beams”. TPS Commissioning and QA: Planning and Monitoring ‐ (Salomons) This session will review publications and other resources relating to TPS commissioning and QA. A knowledge‐based framework for selecting and commissioning a TPS will be presented, focusing on: Plan requirements, Algorithm capabilities, Software design and connectivity, Process integration, and Training. The spatial and dosimetric accuracies demanded of the modern TPS have exceeded the capabilities of our measurement tools. As a result, important information can sometimes be hidden in in the measurement noise. Control charts allow one to distinguish between systematic trends and random noise for commonly repeated measurements such as individual plan measurements for IMRT and VMAT treatments. The application of control charts to such measurements will be presented. Recommendations of MPPG #5 and practical implementation strategies ‐ (Smilowitz) The recently published recommendations from Task Group No. 244, Medical Physics Practice Guideline on Commissioning and QA of Treatment Planning Dose Calculations: Megavoltage Photon and Electron Beams will be presented. The recommendations focus on the validation of commissioning data and dose calculations. Tolerance values for non‐IMRT beam configurations are summarized based on established criteria and data collected by the IROC. More stringent evaluation criteria for IMRT dose calculations are suggested to test the limitations of the TPS dose algorithms for advanced delivery conditions. The MPPG encourages users to create a suite of validation tests for dose calculation for various conditions for static photon beams, heterogeneities, IMRT/VMAT and electron beams. This test suite is intended to be used for subsequent testing, including TPS software upgrades. In the past, the recommendations of some reports have not been widely implemented due to practical limitations. Implementation strategies, tools and processes developed by multiple centers for efficient and “doable” MPPG #5 testing will be presented, as well as a discussion on the overall validation experience. Gamma analysis as a metric for reporting TPS Commissioning and QA results will be discussed. TPS commissioning and QA: Incorporating the entire planning process (Mutic) The TPS and its features do not perform in isolation. Instead, the features and modules are key components in a complex process that begins with CT Simulation and extends to treatment delivery, along with image guidance and verification. Most importantly, the TPS is used by people working in a multi‐disciplinary environment. It is very difficult to predict the outcomes of human interactions with software. Therefore, an interdisciplinary approach to training, commissioning and QA will be presented, along with an approach to the physics chart check and end‐to‐end testing as a tool for TPS QA. The role of standardization and automation in QA will also be discussed. A number of actual TPS defects will be presented along with heuristics for identifying similar defects in the future. Learning Objectives: Identify some of the key documents relevant for TPS commissioning and QA Increase familiarity with the process of commissioning a TPS Learn about the use of Control Charts for TPS QA Understand the new recommendations from MPPG #5 on TPS Dose Algorithm Commissioning and QC/QA Learn practical implementation processes and tools for MPPG #5 validation recommendations Increase awareness of the link between TPS QA and chart checking Review the role of the TPS in the overall planning process Funding Support, Disclosures, and Conflict of Interest: Sasa Mutic: ViewRay Inc.: Grant, Travel Expenses & Honoraria Varian Medical Systems: Grant, Travel Expenses & Honoraria Philips Healthcare: Travel Expenses Siemens: Travel Expenses TreatSafely LLC.: Ownership Radialogica LLC.: Ownership
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.286 | 0.210 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".