Organ doses can be estimated from the computed tomography (CT) dose index for cone-beam CT on radiotherapy equipment
Bibliographic record
Abstract
Cone beam computed tomography (CBCT) systems are fitted to radiotherapy linear accelerators and used for patient positioning prior to treatment by image guided radiotherapy (IGRT). Radiotherapists' and radiographers' knowledge of doses to organs from CBCT imaging is limited. The weighted CT dose index for a reference beam of width 20 mm (CTDIw,ref) is displayed on Varian CBCT imaging equipment known as an On-Board Imager (OBI) linked to the Truebeam linear accelerator. This has the potential to provide an indication of organ doses. This knowledge would be helpful for guidance of radiotherapy clinicians preparing treatments. Monte Carlo simulations of imaging protocols for head, thorax and pelvic scans have been performed using EGSnrc/BEAMnrc, EGSnrc/DOSXYZnrc, and ICRP reference computational male and female phantoms to derive the mean absorbed doses to organs and tissues, which have been compared with values for the CTDIw,ref displayed on the CBCT scanner console. Substantial variations in dose were observed between male and female phantoms. Nevertheless, the CTDIw,ref gave doses within ±21% for the stomach and liver in thorax scans and 2 × CTDIw,ref can be used as a measure of doses to breast, lung and oesophagus. The CTDIw,ref could provide indications of doses to the brain for head scans, and the colon for pelvic scans. It is proposed that knowledge of the link between CTDIw for CBCT should be promoted and included in the training of radiotherapy staff.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.013 | 0.008 |
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".