QUANNOTATE for Quality Assessment of Radiological Images
Bibliographic record
Abstract
Multi-institutional trials involving modern radiotherapy (RT) techniques are unique in that treatment quality can be assessed by reviewing digitized medical images and associated RT structures. Nonetheless, quality assurance (QA) of large-scale trials is always challenging because of time-consuming processes for collecting and reviewing hundreds or thousands of individual cases. We developed QUANNOTATE, a web-application that allows rapid review of large numbers of RT target volumes in an easily accessible format without requiring access to the RT planning system (https://www.quannotate.com). We used QUANNOTATE to evaluate the relationship between target delineation compliance with the international guidelines and treatment outcomes in nasopharyngeal carcinoma (NPC) patients undergoing definitive RT. Despite highly guideline-compliant coverage of critical structures, undercoverage of cavernous sinus was correlated with increased local failure. Data standardization is a key issue in medical image-based radiomics studies, and our data suggest that radiomics analysis should be preceded by detailed QA analysis to ensure outcomes are not confounded due to variance in treatment related factors as opposed to tumor factors.
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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 | 0.031 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.021 |
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".