Evaluation of image‐guidance strategies with helical tomotherapy for localised prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Set-up accuracy of different image-guidance (IG) protocols using reduced imaging frequency was compared with daily IG. Anatomical characteristics were investigated for their potential to help select the suitable IG protocols for individual patients. METHODS: Set-up corrections from 26 prostate cancer patients treated with daily IG on helical tomotherapy were used to simulate IG protocols with reduced imaging frequency, where average set-up corrections from a subset of initial IG sessions were used for subsequent fractions with no IG. Residual set-up error, the difference between the average set-up correction and the actual correction required, was used to evaluate the accuracy of each protocol. Adaptive treatment margins required to encompass these errors were calculated. Body mass index and daily bladder and rectum cross-sectional areas (CSAs) were measured, and their correlations with set-up corrections were evaluated. We also investigated the use of reduced imaging schedules to estimate changes in bladder and rectum CSAs. RESULTS: As expected, residual set-up errors and adaptive treatment margins were effectively reduced with frequent imaging. For the majority of patients (81%), 10 IG sessions were sufficient to reduce residual set-up errors to within the adaptive treatment margins. Daily IG was more suitable than using a reduced IG protocol for a minority of patients (19%) with residual set-up errors that consistently exceeded the margins for >10% of fractions. These patients could be identified with 10 imaging sessions via the analysis of anatomical variations. CONCLUSIONS: The accuracy of modified IG protocols should be validated in the context of institutional practice regarding patient set-up and bowel/bladder preparation.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".