Data from Differences in Natural History between Breast Cancers in <i>BRCA1</i> and <i>BRCA2</i> Mutation Carriers and Effects of MRI Screening-MRISC, MARIBS, and Canadian Studies Combined
Bibliographic record
Abstract
Abstract Background: It is recommended that BRCA1/2 mutation carriers undergo breast cancer screening using MRI because of their very high cancer risk and the high sensitivity of MRI in detecting invasive cancers. Clinical observations suggest important differences in the natural history between breast cancers due to mutations in BRCA1 and BRCA2, potentially requiring different screening guidelines. Methods: Three studies of mutation carriers using annual MRI and mammography were analyzed. Separate natural history models for BRCA1 and BRCA2 were calibrated to the results of these studies and used to predict the impact of various screening protocols on detection characteristics and mortality. Results:BRCA1/2 mutation carriers (N = 1,275) participated in the studies and 124 cancers (99 invasive) were diagnosed. Cancers detected in BRCA2 mutation carriers were smaller [80% ductal carcinoma in situ (DCIS) or ≤10 mm vs. 49% for BRCA1, P < 0.001]. Below the age of 40, one (invasive) cancer of the 25 screen-detected cancers in BRCA1 mutation carriers was detected by mammography alone, compared with seven (three invasive) of 11 screen-detected cancers in BRCA2 (P < 0.0001). In the model, the preclinical period during which cancer is screen-detectable was 1 to 4 years for BRCA1 and 2 to 7 years for BRCA2. The model predicted breast cancer mortality reductions of 42% to 47% for mammography, 48% to 61% for MRI, and 50% to 62% for combined screening. Conclusions: Our studies suggest substantial mortality benefits in using MRI to screen BRCA1/2 mutation carriers aged 25 to 60 years but show important clinical differences in natural history. Impact: BRCA1 and BRCA2 mutation carriers may benefit from different screening protocols, for example, below the age of 40. Cancer Epidemiol Biomarkers Prev; 21(9); 1458–68. ©2012 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".