Evaluating the use of diagnostic CT with flattening filter free beams for palliative radiotherapy: Dosimetric impact of scanner calibration variability
Bibliographic record
Abstract
PURPOSE: Palliative radiotherapy comprises a significant portion of the radiation treatment workload. Volumetric-modulated arc therapy (VMAT) improves dose conformity and, in conjunction with flattening filter free (FFF) delivery, can decrease treatment times, both of which are desirable in a population with a high probability of retreatment with large palliative doses per fraction. Combining FFF and VMAT delivery with planning based on previously acquired diagnostic computed tomography (CT) scans has the potential to further expedite palliative treatment. This study evaluated the dosimetric uncertainty of using FFF beams with VMAT delivery on CT images acquired from different diagnostic vendors, and between different x-ray tube energies, in the palliative setting. METHODS: CT-relative electron density (CT-RED) curves were acquired for the local CT simulator at 100, 120, and 140 kVp, and for two diagnostic CT scanners at 120 kVp. Thirty palliative VMAT plans were recalculated for each CT-RED curve, with 6 MV flat, 6 FFF, and 10 FFF beams. The doses to 95% and 2% of the PTV, the maximum point dose to the spinal canal and esophagus, and the mean dose to the kidneys were compared between recalculated plans. RESULTS: Comparing the dose clouds for a given fluence map calculated with CT-RED curves from different CT scanners at 120 kVp, the mean dose difference was at most 0.3% for each DVH metric. Similar results were reported when comparing dose clouds calculated with CT-RED curves for 100, 120, and 140 kVp on the CT simulator. CONCLUSION: The results of this study confirm that diagnostic scans acquired on machines different from the CT simulator associated with the TPS, are appropriate for VMAT treatment planning in the palliative setting with FFF photon beams.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".