Evaluation of megavoltage CT imaging protocols in patients with lung cancer
Bibliographic record
Abstract
Currently, megavoltage CT studies in most centres with tomotherapy units are performed prior to every treatment for patient set-up verification and position correction. However, daily imaging adds to the total treatment time, which may cause patient discomfort as well as results in increased imaging dose. In this study, four alternative megavoltage CT imaging protocols (images obtained: during the first five fractions, once per week, alternating fractions and daily on alternative weeks) were evaluated retrospectively using the daily position correction data for 42 patients with lung cancer. The additional uncertainty introduced by using a specific protocol with respect to the daily imaging, or residual uncertainty, was analysed on a patient and population bases. The impact of less frequent imaging schedules on treatment margin calculation was also analysed. Systematic deviations were reduced with increased imaging frequency, while random deviations were largely unaffected. Mean population systematic errors were small for all protocols evaluated. In the protocol showing the greatest error, the treatment margins necessary to accommodate residual errors were 1.2, 1.3 and 1.7 mm larger in the left-right, superior-inferior and anterior-posterior directions, respectively, compared with the margins calculated using the daily imaging data. The increased uncertainty because of the use of less frequent imaging protocols may be acceptable when compared with other sources of uncertainty in lung cancer cases, such as target volume delineation and motion because of respiration. Further work needs to be carried out to establish the impact of increased residual errors on dose distribution.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".