In Silico Single-Fraction Stereotactic Ablative Radiation Therapy for the Treatment of Thoracic and Abdominal Oligometastatic Disease With Online Adaptive Magnetic Resonance Guidance
Bibliographic record
Abstract
Purpose Although SABR can improve oncologic outcomes for patients with oligometastatic disease, treatment of metastases near critical organs remains challenging. The purpose of this study is to determine the dosimetric feasibility of delivering magnetic resonance imaging (MRI)-guided adaptive SABR in a single fraction for abdominal and thoracic metastases. Methods and Materials Previously delivered MRI-guided radiation therapy plans for 20 patients with oligometastatic disease in the thorax or abdomen, with 70% (14/20) of the lesions within 8 mm from dose-limiting organs at risk (OARs), were used to simulate the delivery of 24 Gy in a single fraction. Planning objectives included planning target volume (PTV) V 95% >90%, optimized PTV (PTVopt) V 95% >90%, and PTVopt D 99% >20 Gy with no OAR dose violations, where PTVopt removed overlap with nearby planning organ at risk volume (PRV). Single-fraction plans were simulated on the first 5 daily setup breath-hold MRI scans, and the plans were reoptimized to consider variations in setup position and anatomy. Results The mean PTV V 95% for single-fraction SABR plans was lower compared with multifraction plans (mean 85.4% vs 92.6%, P = .02), but mean PTVopt V 95% was not different (95.3% vs 98.2%, P = .62). After reoptimization of the single-fraction plan to the treatment day MRI, there was an increase in mean PTV V 95% (85.0% vs 88.1%, P = .05), increase in mean PTVopt V 95% (92.7% vs 96.3%, P = .02), increase in mean PTVopt D 99% (19.7 Gy vs 23.8 Gy, P < .01), increase in mean frequency of meeting PTV D 99% >20 Gy (52% vs 87%, P < .01), and increase in mean gross tumor volume minimum dose (17.5 Gy vs 19.3 Gy, P < .01). Reoptimization decreased mean frequency of OAR dose constraint violation (48% vs 0%, P < .01). Conclusions Single-fraction MRI-guided SABR is a dosimetrically feasible treatment for oligometastases that allows for on-table adaptation to avoid OAR dose constraint violations, but this method requires clinical validation.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".