Poster — Wed Eve—33: Respiratory Internal Target Volume Assessment Using a Modified Slow CT Scan and CBCT
Bibliographic record
Abstract
The purpose of this study was to investigate the volume of the ITV generated by a modified slow CT scan and CBCT using a QUASAR™ phantom. The standard CT scan (CTstandard) was acquired with pitch 1.7 and scan time 1 second followed by the slow CT scan (CTslow) limited to the tumor region with pitch 1 and scan time 3 seconds. The two image sets were co‐aligned using the same reference point, which was independent of bony anatomy changes with respiration. A gross tumor volume was contoured on each scan; Volume A is for CTstandard and Volume B is for CTslow; The ITV was generated from A∪B in Pinnacle TPS. Nine data sets of CTstandard and CTslow plus CBCT were acquired under free‐breathing. The ITV generated from the CTstandard is smaller than that from the CTslow (80% vs. 93% for 3 cm cube); however, the ITV generated from the co‐alignment of two image sets are improved compared to the sing CT alone. The ITV generated from the co‐alignment of CTstandard and CTslow is 98% for the 3cm cube and acceptable as depicted extent of motion. Modified slow CT scan provides complimentary information for the respiratory tumor motion. It compares favorably with the ITV generated with a cone beam CT. Modified slow CT scan and CBCT with tighter margin were used for lung radiosurgery with daily twice CBCT. It is a useful technique for optimizing the ITV resulting in tighter margins which may reduce toxicity and facilitate dose escalation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".