A novel four-dimensional radiotherapy method for lung cancer: imaging, treatment planning and delivery
Bibliographic record
Abstract
We present treatment planning methods based on four-dimensional computed tomography (4D-CT) to incorporate tumour motion using (1) a static field and (2) a dynamic field. Static 4D fields are determined to include the target in all breathing phases, whereas dynamic 4D fields are determined to follow the shape of the tumour assessed from 4D-CT images with a dynamic weighting factor. The weighting factor selection depends on the reliability of patient breathing and limitations of the delivery system. The static 4D method is compared with our standard protocol for gross tumour volume (GTV) coverage, mean lung dose and V20. It was found that the GTV delineated on helical CT without incorporating breathing motion does not adequately represent the target compared to the GTV delineated from 4D-CT. Dosimetric analysis indicates that the static 4D-CT based technique results in a reduction of the mean lung dose compared with the standard protocol. Measurements on a moving phantom and simulations indicated that 4D radiotherapy (4D-RT) synchronized with respiration-induced motion further reduces mean lung dose and V20, and may allow safe application of dose escalation and CRT/IMRT. The motions of the chest cavity, tumour and thoracic structures of 24 lung cancer patients are also analysed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".