Respiration correlated cone-beam computed tomography and 4DCT for evaluating target motion in Stereotactic Lung Radiation Therapy
Bibliographic record
Abstract
An image-guidance process for using cone-beam computed tomography (CBCT) for stereotactic body radiation therapy (SBRT) of peripheral lung lesions is presented. Respiration correlated CBCT on the treatment unit and four dimensional computed tomography (4DCT) from planning are evaluated for assessing respiration-induced target motion during planning and treatment fractions. Image-guided SBRT was performed for 12 patients (13 lesions) with inoperable early stage non-small cell lung carcinoma. Kilovoltage (kV) projections were acquired over a 360 degree gantry rotation and sorted based on the pixel value of an image-based aperture located at the air-tissue interface of the diaphragm. The sorted projections were reconstructed to provide volumetric respiration correlated CBCT image datasets at different phases of the respiratory cycle. The 4D volumetric datasets were directly compared with 4DCT datasets acquired at the time of planning. For ten of 12 patients treated, the lung tumour motion, as measured by respiration correlated CBCT on the treatment unit, was consistent with the tumour motion measured by 4DCT at the time of planning. However, in two patients, maximum discrepancies observed were 6 and 10 mm in the anterior-posterior and superior-inferior directions, respectively. Respiration correlated CBCT acquired on the treatment unit allows target motion to be assessed for each treatment fraction, allows target localization based on different phases on the breathing cycle, and provides the facility for adaptive margin design in radiation therapy of lung malignancies. The current study has shown that the relative motion and position of the tumour at the time of treatment may not match that of the planning 4DCT scan. Therefore, application of breathing motion data acquired at simulation for tracking or gating radiation therapy may not be suitable for all patients - even those receiving short course treatment techniques such as SBRT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".