Adaptation interactive d'un traitement de radiothérapie par imagerie volumique : Développement et validation d'outils pour sa mise en œuvre en routine clinique
Bibliographic record
Abstract
Changing anatomy during radiotherapy can lead to significant dosimetric consequences for organs at risk (OARs) and/or target volumes. Adaptive radiotherapy can compensate for these variations however its deployment for clinical work is hampered by the increased workload for the medical staff and there is still no commercialized software available for clinical use. We developed a simple in vivo dosimetric alert tool allowing rapid identification of patients who might benefit from an adaptive radiotherapy. Dosimetric evaluation of delivered treatment has been conducted onto 3D on board imaging (CBCT) whose dose calculation accuracy has been evaluated. The tool does not require a new volume of interest delineation. Tool alert is based on objectives and quantifiable criteria defined by the exceeding volumes of interest dose thresholds. Tool precision and detectability have been validated and applied in a retrospective study on 10 head and neck patients. The tool allows detecting patients where an adaptive treatment could have been considered. In its clinical implementation, adaptive radiotherapy process requires deformable matching algorithms to follow patient local's deformations occurring during treatment. Nevertheless, their use has not been validated. We conducted an evaluation of the Block Matching deformable algorithm, suitable for multimodality imaging (CT/CBCT), in comparison to rigid algorithm. A study has been conducted for 10 head and neck patients based on volume of interest contours comparison for 76 CBCT. Similarity parameters used consisted on Dice Similarity Index, Robust Hausdorff Distance (in mm) and the absolute volume difference (in cc)
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".