Radiation exposure of breast tissue in lymphoma radiotherapy: a systematic review of breast dose metrics published since 2000
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We present a systematic review of breast dose metrics reported in lymphoma patients receiving radiotherapy and provide reporting recommendations for breast dose in future publications. METHODS AND MATERIALS: Studies reporting breast doses in lymphoma radiotherapy published between January 2000 and May 2023 were included. Frequency of reporting factors likely to affect breast dose were calculated. Doses for the most frequently reported metrics (mean breast dose (MBD) (Gy, percentage of prescription), V5Gy and V10Gy (%)) were calculated across articles and compared for target volume approaches, radiotherapy techniques, and inclusion of the axilla. RESULTS: Thirty-four distinct breast dose metrics were found across 57 articles. MBD was the most commonly reported. Axilla irradiation significantly increased MBD, V5Gy and V10Gy, yet 21 articles reported breast doses for a mixed cohort with respect to axillary irradiation. Forty-eight of 57 articles did not report the breast contouring guidelines used. Among articles reporting MBD for proton or butterfly-volumetric modulated arc therapy (VMAT), there was no significant reduction in breast radiation dose for protons compared to butterfly-VMAT. INTERPRETATION: A wide variety of breast dose metrics are reported in the literature, making it challenging to pool breast tissue exposure data in lymphoma radiotherapy. Factors shown in individual studies to affect breast dose should be reported more systematically to enable large scale analysis. Reporting the presence/absence of axillary irradiation is crucial, due to the significant effect on breast dose. We provide reporting recommendations for breast dose metrics to improve research into radiotherapy-induced breast cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".