Constrained marker-based VMAT plan optimization towards real-time tumour tracking
Bibliographic record
Abstract
This work investigates the incorporation of visibility parameters and constraints into the optimization of volumetric modulated arc therapy (VMAT) plans using fiducial markers. We propose that by incorporating fiducial marker constraints into the optimization, one may produce treatment plans that ensure a higher visibility of fiducials throughout the irradiation (a requirement for real-time tumour tracking techniques), in addition to simultaneously satisfying dosimetric requirements. We investigated this approach on a dynamic thorax phantom and multiple patient disease sites (prostate, liver and lung) using a radiotherapy optimization development software (MonArc). For all the investigated datasets, three fiducial markers were implanted inside or around the planning target volume (PTV) and a VMAT plan was created for each patient. We modified MonArc to analyze beam’s-eye-views (BEV) of the gantry arc control points to include marker-based visibility constraints of type ‘hard’ (i.e. requiring 100% visibility of all markers, HC) and ‘soft’ (i.e. penalizes visibility for one marker [SC¬I] or two markers [SCII] only) in the optimization process. Dose distributions from the constrained plans (i.e. HC, SCI, and SCII) were compared to the non-constrained plan (NC) using several metrics including the conformity index, homogeneity index, PTV average index and doses to organs-at-risk (OAR). Across all the disease sites, one marker is always fully visible at all BEV apertures (i.e. 100% of the gantry arc control points) for the constrained plans. All three markers were fully visible in at least 33% of BEV apertures for the constrained plans, while also satisfying the required dosimetric objectives. Although dose metrics showed some deterioration for constrained plans (-6% for SCI up to -15% for HC, when compared to NC using the PTV average index), the required dosimetric objectives were still satisfied in at least 90% of patients. In conclusion, we demonstrated that marker-based constraints can be incorporated into VMAT, to produce treatment plans satisfying both the visibility and dosimetric objectives. This approach should ensure greater clinical success when applying real-time tracking algorithms for VMAT delivery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".