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Record W4213378853 · doi:10.1001/jamaoncol.2021.6204

Breast Cancer Screening Strategies for Women With <i>ATM, CHEK2</i>, and <i>PALB2</i> Pathogenic Variants

2022· article· en· W4213378853 on OpenAlexaff
Kathryn P. Lowry, H. Amarens Geuzinge, Natasha K. Stout, Oğuzhan Alagöz, John M. Hampton, Karla Kerlikowske, Harry J. de Koning, Diana L. Miglioretti, Nicolien T. van Ravesteyn, Clyde B. Schechter, Brian L. Sprague, Anna N.A. Tosteson, Amy Trentham‐Dietz, Donald L. Weaver, Martin J. Yaffe, Jennifer M. Yeh, Fergus J. Couch, Chunling Hu, Peter Kraft, Eric C. Polley, Jeanne S. Mandelblatt, Allison W. Kurian, Mark E. Robson, Steven N. Hart, Katherine L. Nathanson, Susan M. Domchek, Christine B. Ambrosone, Hoda Anton‐Culver, Paul L. Auer, Elisa V. Bandera, Leslie Berstein, Kimberly A. Bertrand, Elizabeth S. Burnside, Brian D. Carter, A. Heather Eliassen, Mia M. Gaudet, Christopher Haiman, James M. Hodge, David J. Hunter, Eric J. Jacobs, Esther M. John, Charles Kooperberg, James V. Lacey, Loı̈c Le Marchand, Sara Lindström, Huiyan Ma, Elena Martínez, Susan L. Neuhausen, Polly A. Newcomb, Katie M. O’Brien, Janet E. Olson, Irene M. Ong, Tuya Pal, Julie R. Palmer, Alpa V. Patel, Sonya Reid, Lynn Rosenberg, Dale P. Sandler, Rulla M. Tamimi, Jack A. Taylor, Lauren R. Teras, Celine M. Vachon, Clarice R. Weinberg, Siddhartha Yadav, Song Yao, Argyrios Ziogas, Jeffrey N. Weitzel, David E. Goldgar

Bibliographic record

VenueJAMA Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsCHEK2PALB2MedicineBreast cancerMammographyCancerOncologyPopulationBreast cancer screeningInternal medicineGynecologyEnvironmental healthGeneticsBiology

Abstract

fetched live from OpenAlex

IMPORTANCE: Screening mammography and magnetic resonance imaging (MRI) are recommended for women with ATM, CHEK2, and PALB2 pathogenic variants. However, there are few data to guide screening regimens for these women. OBJECTIVE: To estimate the benefits and harms of breast cancer screening strategies using mammography and MRI at various start ages for women with ATM, CHEK2, and PALB2 pathogenic variants. DESIGN, SETTING, AND PARTICIPANTS: This comparative modeling analysis used 2 established breast cancer microsimulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) to evaluate different screening strategies. Age-specific breast cancer risks were estimated using aggregated data from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium for 32 247 cases and 32 544 controls in 12 population-based studies. Data on screening performance for mammography and MRI were estimated from published literature. The models simulated US women with ATM, CHEK2, or PALB2 pathogenic variants born in 1985. INTERVENTIONS: Screening strategies with combinations of annual mammography alone and with MRI starting at age 25, 30, 35, or 40 years until age 74 years. MAIN OUTCOMES AND MEASURES: Estimated lifetime breast cancer mortality reduction, life-years gained, breast cancer deaths averted, total screening examinations, false-positive screenings, and benign biopsies per 1000 women screened. Results are reported as model mean values and ranges. RESULTS: The mean model-estimated lifetime breast cancer risk was 20.9% (18.1%-23.7%) for women with ATM pathogenic variants, 27.6% (23.4%-31.7%) for women with CHEK2 pathogenic variants, and 39.5% (35.6%-43.3%) for women with PALB2 pathogenic variants. Across pathogenic variants, annual mammography alone from 40 to 74 years was estimated to reduce breast cancer mortality by 36.4% (34.6%-38.2%) to 38.5% (37.8%-39.2%) compared with no screening. Screening with annual MRI starting at 35 years followed by annual mammography and MRI at 40 years was estimated to reduce breast cancer mortality by 54.4% (54.2%-54.7%) to 57.6% (57.2%-58.0%), with 4661 (4635-4688) to 5001 (4979-5023) false-positive screenings and 1280 (1272-1287) to 1368 (1362-1374) benign biopsies per 1000 women. Annual MRI starting at 30 years followed by mammography and MRI at 40 years was estimated to reduce mortality by 55.4% (55.3%-55.4%) to 59.5% (58.5%-60.4%), with 5075 (5057-5093) to 5415 (5393-5437) false-positive screenings and 1439 (1429-1449) to 1528 (1517-1538) benign biopsies per 1000 women. When starting MRI at 30 years, initiating annual mammography starting at 30 vs 40 years did not meaningfully reduce mean mortality rates (0.1% [0.1%-0.2%] to 0.3% [0.2%-0.3%]) but was estimated to add 649 (602-695) to 650 (603-696) false-positive screenings and 58 (41-76) to 59 (41-76) benign biopsies per 1000 women. CONCLUSIONS AND RELEVANCE: This analysis suggests that annual MRI screening starting at 30 to 35 years followed by annual MRI and mammography at 40 years may reduce breast cancer mortality by more than 50% for women with ATM, CHEK2, and PALB2 pathogenic variants. In the setting of MRI screening, mammography prior to 40 years may offer little additional benefit.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.276
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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".

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Citations87
Published2022
Admission routes1
Has abstractyes

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