Efficient on‐line setup correction strategies using plan‐intent functions
Bibliographic record
Abstract
With the introduction of image-guided radiation therapy (IGRT) delivery systems on-line set-up correction strategies have gained in popularity. Usually, the correction workload of these strategies is high compared to off-line strategies as daily setup corrections have to be performed based on a predefined action level. In this work, it is proposed that on-line strategies must not only be judged in terms of workload but also in terms of efficacy. While workload can be easily predicted for such strategies, the efficacy must ultimately reflect the efficiency with which the original treatment plan intent is met. The purpose of this work is to investigate the tradeoff between workload and efficacy of three different on-line set-up correction strategies: The common fixed action level strategy and two novel on-line setup correction strategies, i.e., a dose-volume histogram (DVH) constraint and an equivalent uniform dose (EUD) score strategy that aim directly for better compliance with original treatment plan intent. All strategies were reformulated in terms of a score function that reflected treatment plan intent. A retrospective study was conducted on 5 prostate patients (7-field conformal, 79.8 Gy, 42 fractions). PTV margins were 10 mm except in the posterior direction (7 mm). The original treatment plan intent for these patients was defined using a set of DVH constraints. The results show that the on-line setup correction strategy based on a fixed action level of 3 mm resulted in a considerable correction workload. For larger action levels, a dose benefit (in terms of EUD) in the rectum and bladder was observed for all patients which is clinically "fortuitous" but difficult to take advantage of. In contrast, the application of the two novel strategies generally resulted in a controlled decrease of the dose to the rectum and the bladder with a smaller workload. It is concluded that using information about target anatomy and the planned dose distribution allows the design of specific correction strategies that are better tailored to the individual patient and that comply effectively with initial treatment plan intent.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".