Poster - Thurs Eve-34: Extended CT-range in RT planning of pelvic cancer treatment in presence of hip replacements
Bibliographic record
Abstract
Extended CT range in conventional CT scanners has a potential to allow for a more conformal treatment of patients with hip prosthesis. Its use may limit inaccuracies in electron density maps that are observed due to severe artifacts in CT data. In this study, we investigate the use of CT images with extended CT numbers in dose calculations and compare the results of calculations with standard CT data and measured doses. A phantom containing a hip prosthesis was scanned and treatment was planned with extended and standard CT numbers. The density override function was used to eliminate the effect of artifacts in the region outside of the metallic implant, while raw CT numbers were used to indicate density within the implant. Dose measurements were performed with two types of ion chambers at 6, 10 and 18MV energies. Our results indicate that data with extended CT range result in a better agreement between measured and calculated dose at the central position of the body phantom, as should be expected. However, CT artifacts within the implant region also result in discrepancies between the measured and calculated dose. The discrepancy is greater at lower cross-sectional thickness where bright, high density surface artifacts are high relative to the artificially low density inner region of the implant. Potential ways of resolving the discrepancies are outlined and a possibility of their application to clinical routine will be discussed.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".