Poster — Wed Eve—32: Internal Target Volume Dose Coverage Measurement for Respiratory Tumor Motion
Bibliographic record
Abstract
The purpose of this study was to evaluate the difference between planned and delivered doses within the ITV generated by the co‐alignment of the standard and slow CT scan. A QUASAR™ phantom with a cedar insert (to simulate the lung) and an ionization chamber with buildup cap was scanned twice using modified slow CT scan technique. The respiratory cycle used for CT scan was 4 seconds/cycle and 10.0 mm amplitude along the longitudinal axis of the phantom. The two image sets were co‐aligned using the same reference points. The volume of the ionization chamber was used as a gross tumor volume (GTV) and was contoured on each scan. The ITV was generated by enveloping two GTVs from the two CT scans. The PTV was created by adding a 5mm margin around the ITV. If the chamber movement is within 1 cm, the minimum ITV dose coverage is 98.5%. The ITV dose coverage drops dramatically when the movement is larger than 1cm. The ITV dose coverage drops from 96.3% in 1.1cm motion to 90.7% in 1.5 cm motion. This study evaluated the difference between planned and delivered dose to the GTV using an ITV generated by the co‐alignment of the standard and slow CT scan. Co‐alignment of a slow CT and a standard CT underestimates the amplitude of GTV motion by 6mm. This results in under‐dosing of the ITV by 1.5%. The discrepancy between the TPS and measurement was 0.8%, for a total discrepancy of 2.3 %.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".