Patient-Reported Quality-of-Life Outcomes After Abdominopelvic Stereotactic Body Radiation Therapy Using an MR-Linac System
Bibliographic record
Abstract
PURPOSE: Despite the increasing use of MR-Linac (MRL) in the management of patients with oligometastatic or upper gastrointestinal malignancies, health-related quality-of-life (HRQoL) outcomes are lacking. Currently, it is not well established whether treatments using MRL offer HRQoL advantages over CT-based treatments. In this study, we present prospectively collected HRQoL data after abdominopelvic stereotactic body radiation therapy (SBRT) on a 1.5T MRL system. METHODS AND MATERIALS: We conducted a single-center, prospective observational study of patients receiving MR guided adaptive radiotherapy using the MRL system from September 2019 to November 2024 at Princess Margaret Hospital for abdominopelvic targets. HRQoL assessment was performed using questionnaires from the European Organization for Research and Treatment of Cancer QLQ-C30. Linear mixed-effects models were used to compare the change in the HRQoL domain scores of the QLQ-C30 questionnaire within the MRL cohort from baseline to 3-month and 1-year follow-ups. RESULTS: Seventy-three patients were included in the analysis. Most patients had pelvic (43.8%) or abdominal (34.2%) targets. Following SBRT, 46.6% of the patients received systemic therapy. There was a significant increase in the symptom scale scores for fatigue (β = 8.9; 95% CI = 4.5, 13.2; P < .001) and nausea and vomiting (β = 3.9; 95% CI = 1.2, 6.6; P = .005) at the final fraction compared with baseline. Fatigue remained significantly increased from baseline to the first follow-up at 3 months (β = 4.7; 95% CI = 0.5, 9.0; P = .03). At 12 months, no significant difference was observed in any scale compared with baseline. CONCLUSIONS: Stereotactic radiation treatments using online adaptive MR guided radiation therapy on MRL are well tolerated in patients with abdominopelvic metastases, with HRQoL returning to baseline at 12 months.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".