SU‐FF‐T‐412: The Reliability of Surrogates in Predicting Tumour Motion: A Comparison of Surrogate Based and Non‐Surrogate Based Approach
Bibliographic record
Abstract
Purpose: To assess the interfraction reproducibility of external surrogates in predicting lung tumor motion and to compare that with the reliability of the non‐surrogate approach Methods: 10 patients with fluoroscopically visible non‐operable lung tumors were studied. Data were acquired twice for each patient, pretreatment and post 20 fractions. Chest and abdominal wall surface motion was measured with 3D photogrammetry of retro‐reflective skin markers. Simultaneous time‐stamped A/P fluoroscopic images were used to determine tumor motion in two dimensions. A linear model was formulated to describe the relationship between the surface markers and tumor motion and then applied to predict tumor motion during both the first and second visit. Quantification of the difference between the predicted and actual tumor position demonstrated the surrogate's ability to predict tumor motion. This difference defines the Internal Margins (IM) that must be applied when the surrogate is employed for tracking or gating. Comparison of IM's thus obtained over a four week course of radiotherapy would give a measure of the reliability of the surrogate. Results: Comparison of surrogate based margins on day1 and day20 indicated that prediction ability of the surrogate deteriorated during this time and the required IM' increased by an average of 1.27mm. Furthermore, analysis of non‐surrogate‐based case showed that tumor motion amplitude had increased significantly form day1 to day20 requiring an average increase of 1.16 mm in the IM's. Statistical analysis showed that for both cases assuming a 6mm additional margin would provide the desired tumor coverage with a 99% C.L. Conclusions: Predictive models of motion can reduce the IM component of the PTV by a significant volume, however interfraction reproducibility is poor and the chance of geometric miss is high. Our observations of tumor motion variability suggest that static field margins should also be reassessed over a standard treatment course.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".