Poster — Thurs Eve‐41: Imaging and radiation delivery in helical tomotherapy: Phantom study of a moving target
Bibliographic record
Abstract
Treating lung cancer with radiation therapy by guaranteed delivery of the prescription dose to the target is difficult due to tumour motion. The standard approach to account for motion effects consists of adding a substantial margin to a lesion visible on the CT study. Larger irradiated volume results in increased dose deposition in healthy lung and the potential for patient complications. This investigation focuses on determining the optimal choice of planning CT mode for improved radiation delivery in terms of better target coverage and sparing of healthy organs. Dosimetric measurements were performed on a helical tomotherapy unit. A Quasar® (Modus Medical Devices, London, ON) respiratory phantom was imaged while a polystyrene target moved sinusoidally with a period of 4 s and amplitude of 2 cm. For target moving in superior-inferior and lateral directions, conventional fast-CT image studies were created, as well as maximum intensity projection (MIP) and average intensity projection (AveIP) image studies using four-dimensional CT information. All types of CT studies were used to develop treatment plans with a prescription of 2 Gy per fraction to the target outlined according to the imaged data. Measurements of dose deposition were made in four locations within the moving target using an Exradin A1SL ion chamber. Comparing all results to the dose measured at the centre of the static phantom, the MIP plans overdose the target, the fast-CT results vary from case to case, while the AveIP plans provide consistent dose distribution across the target within 2% of the normalization dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".