Feasibility study for marker‐based VMAT plan optimization toward tumor tracking
Bibliographic record
Abstract
This work investigates the incorporation of fiducial marker-based visibility parameters into the optimization of volumetric modulated arc therapy (VMAT) plans. We propose that via this incorporation, one may produce treatment plans that aid real-time tumor tracking approaches employing exit imaging of the therapeutic beam (e.g., via EPID), in addition to satisfying purely dosimetric requirements. We investigated the feasibility of this approach for a thorax and prostate site using optimization software (MonArc). For a thorax phantom and a lung patient, three fiducial markers were inserted around the tumor and VMAT plans were created with two partial arcs and prescription dose of 48 Gy (4 fractions). For a prostate patient with three markers in the prostate organ, a VMAT plan was created with two partial arcs and prescription dose 72.8 Gy (28 fractions). We modified MonArc to include marker-based visibility constraints ("hard"and "soft"). A hard constraint (HC) imposes full visibility for all markers, while a soft constraint (SC) penalizes visibility for specific markers in the beams-eye-view. Dose distributions from constrained plans (HC and SC) were compared to the reference nonconstrained (NC) plan using metrics including conformity index (CI), homogeneity index (HI), gradient measure (GM), and dose to 95% of planning target volume (PTV) and organs at risk (OARs). The NC plan produced the best target conformity and the least doses to the OARs for the entire dataset, followed by the SC and HC plans. Using SC plans provided acceptable dosimetric tolerances for both the target and OARs. However, OAR doses may be increased or decreased based on the constrained marker location and number of trackable markers. In conclusion, we demonstrate that visibility constraints can be incorporated into the optimization together with dosimetric objectives to produce treatment plans satisfying both objectives. This approach should ensure greater clinical success when applying real-time tracking algorithms, using VMAT delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".