MétaCan
Menu
← Back to cohort
Record W2011407375 · doi:10.1200/jco.2014.56.2975

How Well Does Supplemental Screening Magnetic Resonance Imaging Work in High-Risk Women?

2014· letter· en· W2011407375 on OpenAlexaboutno aff
Wendie A. Berg

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMagnetic resonance imagingOncologyRadiology

Abstract

fetched live from OpenAlex

There has been much recent criticism of breast cancer screening. Long-term results of randomized controlled trials of mammography on average show a decrease in breast cancer mortality of 22% in women age 50 to 74 years and 15% in women age 39 to 49 years. This benefit to mammography is offset by high rates of false positives, with 10% of women recalled for additional testing each year to diagnose cancer in 0.2% to 0.7% of women screened. Further, at least some of the cancers found with screening mammography would never otherwise be diagnosed in the patient’s lifetime. Such overdiagnosis results in potentially harmful treatments. The magnitude of such overdiagnosis is a topic of much debate, but is likely to represent at least 10% of all breast cancers found on screening mammography. Most such overdiagnosis is likely due to low-grade ductal carcinoma in situ (DCIS), and some subcentimeter low-grade, estrogen receptor–positive invasive cancers. The ability to stratify women on the basis of breast cancer risk presents additional opportunities and challenges for screening, particularly with discovery of disease-associated variations in the susceptibility genes BRCA1 and BRCA2. In the article that accompanies this editorial, one of the first challenges to be overcome by Chiarelli et al in the Ontario Breast Screening Program (OBSP) was that of assessing the family history and other risk factors of women who might be eligible for high-risk screening and performing genetic assessment as appropriate. Bellcross et al estimated that approximately 6% of women in Detroit have a family history of breast and/or ovarian cancer that met the 2005 United States Preventive Services criteria for genetic counseling. However, after providing such history, in a 2008 survey, only 20% of women at risk were correctly identified by their practitioners and referred for such counseling. It is not clear what the success rate is in the OBSP for identifying and referring such women for counseling or what percentage of women in the OBSP are considered high risk. It is also not explicitly clear what family history criteria or risk assessment tools were used by primary care physicians in Ontario; many such tools have been validated (reviewed in Nelson et al). Based on family history, after women were referred for counseling, the IBIS or BOADICEA models were used to identify women eligible for high-risk screening ( 25% lifetime risk of breast cancer), and 1,629 of 5,201 (31.3%) of those referred were eligible. Women who were known to have pathogenic mutations in BRCA1 or BRCA2 or other predisposing pathogenic mutations or prior chest radiation therapy before age 30 years and at least 8 years earlier, as well as first-degree untested relatives of such patients, were also eligible for high-risk screening. The OBSP recommendation that high-risk women age 30 to 69 years undergo annual screening with magnetic resonance imaging (MRI) as a supplement to digital mammography parallels that of the American Cancer Society. Women with a personal history of breast cancer who met other high-risk criteria were included and comprised 226 of 2,290 (9.9%) of the population studied, though details of results in this subgroup are not provided. No trials have shown that screening mammography reduces breast cancer mortality in high-risk women. Several small studies have shown reduced mammographic sensitivity, more node-positive disease, and higher interval cancer rates (ie, rates of cancer detected clinically in the interval between screens) in known or suspected BRCA1/2 carriers compared with average-risk women. Compared with women without pathogenic mutations, aggressive, estrogen receptor–negative, and triple-negative cancers are more common in BRCA1/2 carriers and tend to occur at a younger age, especially in BRCA1 carriers. Although prophylactic mastectomy greatly reduces the risk of breast cancer and of death as a result of breast cancer in women with pathogenic mutations (reviewed in Nelson et al), women who undergo mastectomy experience pain and reduced enjoyment of sex, thus making effective methods of imaging surveillance a desirable alternative. To what extent does the addition of annual MRI to mammography in high-risk women increase cancer detection compared with mammography alone? Chiarelli et al reported detection of 15 cancers in 813 screens (18.5 per 1,000; 95% CI, 10.2 to 29.7) among BRCA1 or BRCA2 carriers as a result of the first screening MRI in an organized screening program. Among 1,158 women screened as a result of family history (lifetime risk 25%), six (5.2 per 1,000, 95% CI, 1.9 to 11.2) were found to have cancer only on MRI in the first screening round. Another two cancers were found in women with prior chest radiation therapy before age 30 years. Of the total 23 cancers found by MRI alone, 17 (74%) were invasive. This represents an early report on a program started in July 2011 with follow-up through March 2013, so many important parameters remain to be assessed. Analysis of the benefits of screening in high-risk women will require consideration of factors other than cancer detection rates alone. Chiarelli et al did not report further detail of the cancers found only with MRI. Molecular subtype and node status will be important to know for invasive cancers, and additional analyses are planned. In a prior elegant analysis of Warner et al encompassing 435 BRCA1/2 carriers who underwent MRI in Ontario and 830 controls matched for JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 32 NUMBER 21 JULY 2

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)0.015
metaresearch head score (Gemma)0.123
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.123
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.028
GPT teacher head0.356
Teacher spread0.328 · 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".

Quick stats

Citations15
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicBRCA gene mutations in cancer→French-language works237,207→