Clinical impact of radiotherapy quality assurance results in contemporary cancer trials: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Radiotherapy quality assurance (RTQA) is a critical aspect of randomized controlled trials (RCTs) and is associated with validity and reproducibility of the study findings. We conducted a systematic review and meta-analysis to assess the impact of RTQA results in contemporary RCTs on patient outcomes. METHODS: We searched MEDLINE and CENTRAL from January 2010, to April 2024, for papers that report on the impact of RTQA on patient outcomes in contemporary RCTs. We conducted random-effects meta-analyses to examine the association of radiotherapy protocol deviations with overall survival (OS), progression free survival (PFS), and locoregional recurrence (LR). RESULTS: Of 2,723 citations, 16 publications reporting on 13 RCTs were included across various disease sites. Of 7,170 total randomized patients across 1,076 institutions in over 25 countries, 5,560 patients had radiotherapy quality data and were included in RTQA analyses. Most included RCTs (7/12; 58 %) conducted exclusively retrospective RTQA after treatment completion. Our meta-analyses found that protocol deviations may be associated with worse OS [HR = 1.65 (95 % CI: 1.23-2.22; p < 0.001)] and PFS [HR = 1.79 (95 % CI: 1.00-3.21; p = 0.03)]. No significant association was demonstrated between protocol deviations and LR [HR = 2.09 (95 % CI: 0.85-5.15; p = 0.108)]. CONCLUSIONS: Quality of radiotherapy continues to have an important, measurable impact on patient outcomes in oncology RCTs, and rigorous, real-time RTQA procedures may diminish these effects by standardizing RT. Future trials should provide patient outcome data in relation to RTQA and continue to report on the effect of protocol deviations in the context of modern RT techniques.
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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 | 0.102 | 0.253 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.059 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".