Optimizing SABR delivery for synchronous multiple lung tumors using volumetric-modulated arc therapy
Bibliographic record
Abstract
Background: Volumetric-modulated arc therapy (VMAT) delivery for stereotactic ablative radiotherapy (SABR) of multiple lung tumors allows for faster treatments. We report on clinical outcomes and describe a general approach for treatment planning.Material and methods: Patients undergoing multi iso-center VMAT-based SABR for ≥2 lung lesions between 2009 and 2014 were identified from the VU University Medical Center and London Health Sciences Centre. Patients were eligible if the start date of the SABR treatment for the different lesions was within a time range of 30 days. SABR was delivered using separate iso-centers for lesions at a substantial distance from each other. Tumors were either treated with a single fraction of 34 Gy, or using three risk-adapted dose-fractionation schemes, namely three fractions of 18 Gy, five fractions of 11 Gy, or eight fractions of 7.5 Gy, depending on the tumor size and the location. Multivariable analysis was performed to assess factors predictive of clinical outcomes.Results: Of 84 patients (188 lesions) identified, 46% were treated for multiple metastases and 54% for multiple primary NSCLC. About 97% were treated for two or three lesions, and 56% had bilateral disease. After a median follow-up of 28 months, median overall survival (OS) for primary tumors was 27.6 months, and not reached for metastatic lesions (p = .028). Grade ≥3 toxicity was observed in 2% of patients. Multivariable analysis showed that grade 2 or higher radiation pneumonitis (n = 9) was best predicted by a total lung V35Gy of ≥6.5% (in 2Gy/fraction equivalent) (p = .007).Conclusion: Severe toxicity was uncommon following SABR using VMAT for up to three lung tumors. Further investigations of planning parameters are needed in patients presenting with more lesions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".