Sci‐PM Sat ‐ 09: Respiratory gating in cancer applications, including 4‐D CT based treatment planning
Bibliographic record
Abstract
Respiratory gating is one way to compensate for organs and/or tumours that are affected by respiratory motion. One approach to respiratory gating is to use the Real‐Time Position Management (RPM) Respiratory Gating System, by Varian Medical Systems, which records the cyclic motion of the chest or abdomen using fiducial skin markers while patients breathe freely. There is conflicting evidence, however, regarding the prediction of internal organ motion with external motion. Moreover, treatment planning is typically based on helical CT scans that are also acquired while patients breathe freely. Consequently, the relative position of the exterior body, the target, and the critical organs at any one phase of a breathing cycle cannot be determined accurately, and the choice of the “beam on” time could lead to error. We have developed a method in acquiring a 4‐D CT data set without the aid of external markers. Instead, the method registers images based on internal correlation at tissue interfaces between two successive respiratory phases. The 3‐D motion of the exterior body and internal organs/tumours can be derived and correlated with the RPM signal if acquired simultaneously. If strong correlation exists, then the set of images can be used for detailed treatment planning to determine the optimal treatment phase of the breathing cycle. Eighteen patients have been imaged with 4‐D CT, and one has been treated with 4‐D CT based respiratory gating. We present our results in the context of the first lung cancer patient in our clinic that was treated with this method.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".