The development of a 4D treatment planning methodology to simulate the tracking of central lung tumors in an <scp>MRI</scp>‐linac
Bibliographic record
Abstract
PURPOSE: Targeting and tracking of central lung tumors may be feasible on the Elekta MRI-linac (MRL) due to the soft-tissue visualization capabilities of MRI. The purpose of this work is to develop a novel treatment planning methodology to simulate tracking of central lung tumors with the MRL and to quantify the benefits in OAR sparing compared with the ITV approach. METHODS: Full 4D-CT datasets for five central lung cancer patients were selected to simulate the condition of having 4D-pseudo-CTs derived from 4D-MRI data available on the MRL with real-time tracking capabilities. We used the MRL treatment planning system to generate two plans: (a) with a set of MLC-defined apertures around the target at each phase of the breathing ("4D-MRL" method); (b) with a fixed set of fields encompassing the maximum inhale and exhale of the breathing cycle ("ITV" method). For both plans, dose accumulation was performed onto a reference phase. To further study the potential benefits of a 4D-MRL method, the results were stratified by tumor motion amplitude, OAR-to-tumor proximity, and the relative OAR motion (ROM). RESULTS: With the 4D-MRL method, the reduction in mean doses was up to 3.0 Gy and 1.9 Gy for the heart and the lung. Moreover, the lung's V12.5 Gy was spared by a maximum of 300 cc. Maximum doses to serial organs were reduced by up to 6.1 Gy, 1.5 Gy, and 9.0 Gy for the esophagus, spinal cord, and the trachea, respectively. OAR dose reduction with our method depended on the tumor motion amplitude and the ROM. Some OARs with large ROMs and in close proximity to the tumor benefited from tracking despite small tumor amplitudes. CONCLUSIONS: We developed a novel 4D tracking methodology for the MRL for central lung tumors and quantified the potential dosimetric benefits compared with our current ITV approach.
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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 | 0.003 | 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 | 0.001 | 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".