Quantification of interobserver variability in image registration using cone beam CT for partial bladder radiotherapy—a comparison between lipiodol and bladder wall surface
Bibliographic record
Abstract
The use of lipiodol or bladder wall surface (BWS) for image guidance has improved the treatment quality for partial bladder irradiation. Currently, this procedure is manually performed by different users. This study assessed the interobserver variability of using image guidance for partial bladder irradiation. 7 observers were prospectively recruited to manually register 5 cone beam CTs (CBCT) from each of 20 bladder cancer patients with lipiodol injected for tumor demarcation. Lipiodol and BWS were used to register the CBCT to pre-treatment reference images, and displacement values in three directions were collected. Mean difference among observers and the 95% limit of agreement were calculated to measure interobserver variability. Margin required and the resultant treatment volume were compared between the surrogates. A total of 4200 displacement values were collected for analysis. Lipiodol was superior to BWS, with a mean difference among observers of <2 mm and a 95% limit of agreement of <5 mm in all directions. Of the three directions, greatest variability was observed in the superior–inferior direction for both surrogates, hence requiring a larger margin than the other two directions. After applying the corresponding margin, the mean volume of BWS-planning target volume was calculated to be significantly larger than lipiodol-planning target volume (166 cm3 vs 134 cm3, p < 0.05). The use of lipiodol achieved a higher interobserver agreement than BWS. A larger margin in the superior–inferior direction is recommended due to greater interobserver variability observed in this direction for both surrogates. The uncertainty associated with the image registration by multiple observers for bladder image-guided radiotherapy is quantified for two surrogates.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".