The Relative Biological Effectiveness of Low-Dose Mammography Quality X Rays in the Human Breast MCF-10A Cell Line
Bibliographic record
Abstract
Mammography is used to screen a large fraction of the population for breast cancer, and mammography quality X rays are speculated to be more damaging than the higher energy X rays used for other diagnostic procedures. The radiation dose delivered to breast cells as a result of these screening exposures may be a concern. The purpose of this current study was to determine the relative biological effectiveness (RBE) of low-energy mammography X rays for radiation-induced DNA double-strand breaks evaluated using a highly sensitive automated 53BP1 assay. Automation of the 53BP1 assay enabled the quantification and analysis of meaningful image-based features, including foci counting, within the cell nuclei. Nontumorigenic, human breast epithelial MCF-10A cells were irradiated in the low-dose range with approximately 3-30 mGy of 29 kVp mammography X rays or (137)Cs (662 keV) gamma rays. The induction and resolution of the 53BP1 foci did not differ significantly between exposures to (137)Cs gamma rays and 29 kVp X rays. The RBE was calculated to be 1.1 with a standard deviation of 0.2 for the initial number of radiation-induced double-strand breaks. The radiation dose from a single mammogram did not yield a significant change in the number of detectable foci. However, analysis of additional features revealed subtle differences in the distribution of 53BP1 throughout the nuclei after exposure to the different radiation qualities. A single mammogram was sufficient to alter the distribution of 53BP1 within the nuclear area, but not into discrete foci, while a dose-matched gamma exposure was not sufficient to alter the distribution of 53BP1. Our results indicate that exposure to clinically relevant doses of low-energy mammography quality X rays does not induce more DNA double-strand breaks than exposure to higher energy photons.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".