Evaluation of the validity of a convolution method for incorporating tumour movement and set-up variations into the radiotherapy treatment planning system
Bibliographic record
Abstract
Modern radiotherapy techniques have developed to a point where the ability to conform to a particular tumour shape is limited by organ motion and set-up variations. The result is that dose distributions displayed by treatment planning systems based on static beam modelling are not representative of the dose received by the patient during a fractionated course of radiotherapy. The convolution-based method to account for these variations in radiation treatment planning systems has been suggested in previous work. The validity of the convolution method is tested by comparing the dose distribution obtained from this convolution method with the dose distribution obtained by summing the contribution to the total dose from each fraction of a fractionated treatment (for increasing numbers of fractions) and simulating random target position variations between fractions. For larger numbers of fractions (approximately or > 15) which are the norm for radical treatment schemes, it is clear that incorporation of movement by a convolution method could potentially produce a more accurate dose distribution. There are some limitations that have been identified, however, especially in relation to the heterogeneous nature of patient tissues, which require further investigation before the technique could be applied clinically.
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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 | 0.001 | 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".