Targeting the Tumor: Assessing the Impact of Bladder Volume and Position on Accuracy of Radiation Delivery for Patients with Bladder Cancer
Bibliographic record
Abstract
Context Daily variations in bladder size and position can negatively impact the ability to accurately deliver radiation. Aims We attempted to quantify how bladder volumes and positions change over the course of radiotherapy for muscle invasive bladder cancer and the planning target volume (PTV) margins required to account for such changes. Methods and material Cone-beam computed tomography (CT) images of 28 patients during their first, second, and third fractions and weekly thereafter were acquired. Bladders were contoured and the volume, centre of mass, and the maximal positions were recorded and compared to the planning CT scan. Statistical analysis Bladder parameters were analysed using regression analysis examining for time trends and correlation to the patient, tumour, or treatment-related factors. Results There was great variability in the mean bladder volumes during the radiotherapy courses (154.17 +/- 129.38 cm3). There were no statistically significant trends for volume changes. Deviations in bladder positions were seen but were small in magnitude. No patient factors were identified which could help predict bladder changes clinically. Bladder variability resulted in a high percentage of fractions (39.6%) in which part of the bladder was outside the PTV. Calculated PTV margins (for 90% of the population to receive 95% of the prescription dose) were 1.48 cm right, 1.15 cm left, 2.13 cm posterior, 1.52 cm anterior, 2.23 cm superior, and 0.52 cm inferior. Conclusions Because of random bladder changes, a significant number of fractions were treated in which the clinical target volume (CTV) fell outside of the PTV. Methods to minimize the amount of CTV that is missed on a fraction to fraction basis should be explored.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
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".