SU-E-J-06: Characterization of Motion Baseline Variations in Lungs Using CBCT Imaging
Bibliographic record
Abstract
Purpose: To characterize the motion baseline for lung tumors using cone-beam CT images and to verify if it is possible to adjust PTV margins during treatment based on the patientˈs anatomy and reduce OARs dose.Methods: A set of cone-beam CT images from 28 patients each having between 1 and 20 CBCTs was used to evaluate the temporal evolution of the lung tumorˈs position and volume. GTVs have been contoured for each CBCT and compared with those on the corresponding planning CT images. Planning contours were done by a radiation oncologist and all CBCT contours were reviewed by radiation oncologist resident. We then extracted the volume and center of mass coordinates. Furthermore, points have been placed at extremities of tumors in axial, sagittal and coronal axis for each CBCT in order to characterize the maximum extent of the GTV in all directions.Results: Determination of volume variation shows that the set of patients have an average loss of 42% of their tumors volume over the entire course of treatments (on average treatment ended 48 days after the plan CT was acquired) and an average loss of 14% at the first CBCT after a mean time of 21 days since the planning CT. The tumor center of mass had an average displacement of 0.85 cm during a mean time of 48 days with a maximum of 2.03 cm and a minimum of 0.16 cm, and move equally in lateral, AP and SI axis, but has greater movement in left, anterior and superior directions. Conclusions: This study shows that there is a non-negligible volume reduction and a significant tumor displacement of lung tumors to adjust PTV margins, especially for patients with atelectasis. We could probably reduce OARs dose with more conformal contours.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".