Standardizing Normal Tissue Contouring for Radiation Therapy Treatment Planning: An ASTRO Consensus Paper
Bibliographic record
Abstract
PURPOSE: The comprehensive identification and delineation of organs at risk (OARs) are vital to the quality of radiation therapy treatment planning and the safety of treatment delivery. This guidance aims to improve the consistency of ontouring OARs in external beam radiation therapy treatment planning by providing a single standardized resource for information regarding specific OARs to be contoured for each disease site. The guidance is organized in table format as a quality assurance tool for practices and a training resource for residents and other radiation oncology students (see supplementary materials). METHODS AND MATERIALS: The Task Force formulated recommendations based on clinical practice and consensus. The draft manuscript was peer reviewed by 16 reviewers, the American Society for Radiation Oncology (ASTRO) legal counsel, and ASTRO's Multidisciplinary Quality Assurance Subcommittee and revised accordingly. The recommendations were posted on the ASTRO website for public comment in June 2018 for a 6-week period. The final document was approved by the ASTRO Board of Directors in August 2018. RESULTS: Standardization improves patient safety, efficiency, and accuracy in radiation oncology treatment. This consensus guidance represents an ASTRO quality initiative to provide recommendations for the standardization of normal tissue contouring that is performed during external beam treatment planning for each anatomic treatment site. Table 1 defines 2 sets of structures for anatomic sites: Those that are recommended in all adult definitive cases and may assist with organ selection for palliative cases, and those that should be considered on a case-by-case basis depending on the specific clinical scenario. Table 2 outlines some of the resources available to define the parameters of general OAR tissue delineation. CONCLUSIONS: Using this paper in conjunction with resources that define tissue parameters and published dose constraints will enable practices to develop a consistent approach to normal tissue evaluation and dose documentation.
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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 | 0.175 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".