Characterization of lung tumors motion baseline using cone‐beam computed tomography
Bibliographic record
Abstract
PURPOSE: To characterize the interfractional variability in lung tumor volume, position, and tumor boundaries. METHODS: Cone-beam computed tomography (CBCT) scans were acquired weekly during the course of treatment for 34 lung cancer patients (1-20 scans) with large tumors. Spatial registration based on bones was performed between contoured planning CT and CBCT. Gross tumor volume (GTV) on each CBCT was then contoured. Tumor volume, centroid, and boundaries variability were quantified. A commercial deformable registration software was tested and results were compared to manual contours. RESULTS: Mean volume reduction was 41 ± 32% (p < 0.001) after an average time of 51 days. Tumor centroid drifts were 0.03, 0.14, and -0.13 cm in right-left (RL), anterior-posterior (AP), and superior-inferior (SI) directions with standard deviations of 0.55, 0.50, and 0.51 cm. GTV boundaries displacements were -0.27, -0.14, and -0.16 cm with standard deviations of 0.64, 0.57, and 0.59 cm in RL, AP, and SI directions. Relative error between deformed and manual contours with the commercial deformable registration software rose up exponentially with the GTV decrease. CONCLUSIONS: GTV size changes for large lung tumors are similar to those for standard tumors. Magnitude absolute values of displacement vector for centroid and boundaries shifts show that there is not a preferred direction for the drifts but shrinkage.
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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 | 0.000 | 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 | 0.000 | 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 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".