Can Polymetastatic Disease Be ARRESTed Using SABR? A Dosimetric Feasibility Study to Inform Development of a Phase 1 Trial
Bibliographic record
Abstract
PURPOSE: Phase 2 randomized trials suggest that stereotactic ablative radiation therapy improves progression-free and overall survival in patients with oligometastatic cancer, with phase 3 trials currently testing stereotactic ablative radiation therapy in up to 10 metastases. Whether stereotactic radiation therapy could provide similar benefits in polymetastatic disease (>10 metastases) is unknown. We sought to evaluate the dosimetric feasibility of using stereotactic radiation therapy in polymetastatic disease in preparation for a phase 1 trial. METHODS AND MATERIALS: Five craniospinal computed tomography simulations were used to simulate 24 metastatic targets (n = 2 patients), 30 targets (n = 2 patients), and 50 targets (n = 1 patient) that were not present on the initial scan. Creation of radiation therapy plans was attempted for doses up to 30 Gy in 5 fractions, with de-escalation to 24 Gy/4, 18 Gy/3, 12 Gy/2, or 6 Gy/1 if not feasible based on standardized dose constraints. Plans were created using Raystation for delivery on linear accelerators using volumetric modulated arc therapy and validated using Mobius 3D. RESULTS: A stereotactic radiation therapy treatment plan was generated for each simulated patient. Dose constraints were met to a dose of 30 Gy in 5 fractions for the patients with 24 and 30 lesions. For the patient with 50 targets, dose de-escalation to 12 Gy in 2 fractions was required to meet lung constraints. Estimated beam-on time varied between 18 and 29 minutes per fraction of 6 Gy. Median D95 planning target volume dosimetry ranged from 96.6% to 97.7% of the prescription dose. The conformity index (R100) range was 0.89 to 0.95, and R50 range was 6.84 to 8.72. CONCLUSIONS: Stereotactic radiation therapy treatment plans meeting standardized dose constraints could be created in the setting of 24 to 50 metastatic lesions using volumetric modulated arc therapy. This safety of this approach is being evaluated in a phase 1 trial (NCT04530513).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".