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Record W4394760621 · doi:10.1001/jamaoncol.2023.6944

MRI Surveillance and Breast Cancer Mortality in Women With <i>BRCA1</i> and <i>BRCA2</i> Sequence Variations

2024· article· en· W4394760621 on OpenAlexafffund
Jan Lubiński, Joanne Kotsopoulos, Pål Møller, Tuya Pal, Andrea Eisen, Larissa Peck, Beth Y. Karlan, Amber M. Aeilts, Charis Eng, Louise Bordeleau, William D. Foulkes, Nadine Tung, Fergus J. Couch, Robert Fruscio, Teresa Ramón y Cajal, Christian F. Singer, Susan L. Neuhausen, Dana Zakalik, Cezary Cybulski, Jacek Gronwald, Tomasz Huzarski, Klaudia Stempa, J.S. Dungan, Carey A. Cullinane, Olufunmilayo I. Olopade, Kelly Metcalfe, Ping Sun, Steven A. Narod, Kevin Sweet, Leigha Senter, Howard M. Saal, Lea Velsher, Susan Randall Armel, Jeanna McCuaig, Seema Panchal, Aletta Poll, Edmond G. Lemire, Kim Serfas, Richard B. Reilly, Josephine Wagner Costalas, Stephanie A. Cohen, Joanne L. Blum

Bibliographic record

VenueJAMA Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsMcGill UniversityJuravinski Cancer CentrePrincess Margaret Cancer CentrePublic Health OntarioWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineBreast cancerBreast MRIHazard ratioMagnetic resonance imagingCancer registryProportional hazards modelCancerMammographyDemographyGynecologyOncologyInternal medicineConfidence intervalRadiology

Abstract

fetched live from OpenAlex

Importance: Magnetic resonance imaging (MRI) surveillance is offered to women with a pathogenic variant in the BRCA1 or BRCA2 gene who face a high lifetime risk of breast cancer. Surveillance with MRI is effective in downstaging breast cancers, but the association of MRI surveillance with mortality risk has not been well defined. Objective: To compare breast cancer mortality rates in women with a BRCA1 or BRCA2 sequence variation who entered an MRI surveillance program with those who did not. Design, Setting, and Participants: Women with a BRCA1 or BRCA2 sequence variation were identified from 59 participating centers in 11 countries. Participants completed a baseline questionnaire between 1995 and 2015 and a follow-up questionnaire every 2 years to document screening histories, incident cancers, and vital status. Women who had breast cancer, a screening MRI examination, or bilateral mastectomy prior to enrollment were excluded. Participants were followed up from age 30 years (or the date of the baseline questionnaire, whichever was later) until age 75 years, the last follow-up, or death from breast cancer. Data were analyzed from January 1 to July 31, 2023. Exposures: Entrance into an MRI surveillance program. Main Outcomes and Measures: Cox proportional hazards modeling was used to estimate the hazard ratios (HRs) and 95% CIs for breast cancer mortality associated with MRI surveillance compared with no MRI surveillance using a time-dependent analysis. Results: A total of 2488 women (mean [range] age at study entry 41.2 [30-69] years), with a sequence variation in the BRCA1 (n = 2004) or BRCA2 (n = 484) genes were included in the analysis. Of these participants, 1756 (70.6%) had at least 1 screening MRI examination and 732 women (29.4%) did not. After a mean follow-up of 9.2 years, 344 women (13.8%) developed breast cancer and 35 women (1.4%) died of breast cancer. The age-adjusted HRs for breast cancer mortality associated with entering an MRI surveillance program were 0.20 (95% CI, 0.10-0.43; P < .001) for women with BRCA1 sequence variations and 0.87 (95% CI, 0.10-17.25; P = .93) for women with BRCA2 sequence variations. Conclusion and Relevance: Results of this cohort study suggest that among women with a BRCA1 sequence variation, MRI surveillance was associated with a significant reduction in breast cancer mortality compared with no MRI surveillance. Further studies of women with BRCA2 sequence variations are needed to ascertain these women obtain the same benefits associated with MRI surveillance.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.302
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations73
Published2024
Admission routes2
Has abstractyes

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