Po‐Poster ‐ 21: On the use and sensitivity of dose‐functional volume histograms in radiation treatment planning
Bibliographic record
Abstract
With the increased use of multi‐modality imaging in radiation oncology, dose functional volume histograms (DfVHs) have been introduced as a simple and insightful means of incorporating healthy and diseased tissue functional information in treatment evaluation. The DfVHs themselves may also be used to estimate radiobiological metrics such as the Equivalent Uniform Dose (EUD) or probability of tumor control and normal tissue damage. As the concepts of the DfVH and its utility in computing radiobiological metrics are still in development, the purpose of this work was to investigate the interpretation and sensitivities of DfVH curves and subsequently deduced radiobiological estimates by using mathematically defined and perturbed functional image data sets. Factors such as the signal to noise ratio of the functional image, the relationship between signal intensity of the functional image to the number of functional sub‐units in the voxel, and the extent of mis‐alignment of the functional image to the dose distribution all may affect the shape of the DfVH. As a consequence radiobiological metrics, such as the EUD, are also sensitive to these factors and caution is recommended when using these functions as a predictive measure of outcome. Despite this, the simplicity and relative robustness of the DfVH provides an efficient means of ranking the quality of competing treatment plans. This work was funded through a grant from the MDS‐Nucletron CCO Medical Physics Fund.
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".