QUANNOTATE for Quality Assessment of Radiological Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it