TH-AB-BRA-08: Simulated Tumor Tracking in An MRI Linac for Lung Tumor Lesions Using the Monaco Treatment Planning System
Bibliographic record
Abstract
Purpose: To simulate tumor tracking in an Elekta MRI-linac (MRL) and to compare this tracking method with our current ITV approach in terms of OAR sparing for lung cancer patients. Methods: Five SABR-NSCLC patients with central lung tumors were selected for reasons of potential enhancement of tumor-tissue delineation using MRI. The Monaco TPS was used to compare the current clinical ITV approach to a simulated, novel tracking method which used a 7MV MRL beam in the presence of an orthogonal 1.5 T magnetic field (4D-MRL method). In the simulated tracking scenario, achieved using the virtual couch shift (VCS), the PTV was defined using an isotropic 5mm margin applied to the GTV of each phase, as acquired from an 8-phase amplitude-binned 4DCT. These VCS plans were optimized and weighted on each phase. The dose weighting was performed using the patient-specific breathing traces. The doses were accumulated on the inhale phase. The two methods were compared by assessing the OAR DVHs. Results: The 4D-MRL method resulted in a reduced target volume (by an average of 29% over all patients). The benefits of using an MRL tracking system depended on the tumor motion amplitude and the relative OAR motion (ROM) to the target. The reduction in mean doses to parallel organs was up to 3 Gy for the heart and 2.1 Gy for the lung. The reductions in maximum doses to serial organs were up to 9.4 Gy, 5.6 Gy, and 8.7 Gy for the esophagus, spinal cord, and the trachea, respectively. Serial organs benefited from MRL tracking when the ROM was ≥ 0.3 cm despite small tumor motion amplitude in some cases. Conclusions: This work demonstrated the potential benefit for an MRL tracking system to spare OARs in SABR-NSCLC patients with central tumors. The benefits are embodied in the target volume reduction. This project was made possible with the financial support of Elekta.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".