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Record W4403146221

Supplemental Screening as an Adjunct to Mammography for Breast Cancer Screening in People With Dense Breasts: A Health Technology Assessment.

2023· review· en· W4403146221 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2023
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsAdjunctMammographyMedicineBreast cancer screeningBreast cancerMedical physicsCancerRadiologyInternal medicine
DOInot available

Abstract

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Background: Screening with mammography aims to detect breast cancer before clinical symptoms appear. Among people with dense breasts, some cancers may be missed using mammography alone. The addition of supplemental imaging as an adjunct to screening mammography has been suggested to detect breast cancers missed on mammography, potentially reducing the number of deaths associated with the disease. We conducted a health technology assessment of supplemental screening with contrast-enhanced mammography, ultrasound, digital breast tomosynthesis (DBT), or magnetic resonance imaging (MRI) as an adjunct to mammography for people with dense breasts, which included an evaluation of effectiveness, harms, cost-effectiveness, the budget impact of publicly funding supplemental screening, the preferences and values of patients and health care providers, and ethical issues. Methods: We performed a systematic literature search of the clinical evidence published from January 2015 to October 2021. We assessed the risk of bias of each included study using the Cochrane Risk of Bias or RoBANS tools, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature review and conducted cost-effectiveness analyses with a lifetime horizon from a public payer perspective. We also analyzed the budget impact of publicly funding supplemental screening as an adjunct to mammography for people with dense breasts in Ontario. To contextualize the potential value of supplemental screening for dense breasts, we spoke with people with dense breasts who had undergone supplemental screening; performed a rapid review of the qualitative literature; and conducted an ethical analysis of supplemental screening as an adjunct to mammography. Results: We included eight primary studies in the clinical evidence review. No studies evaluated contrast-enhanced mammography. Nonrandomized and randomized evidence (GRADE: Very low to Moderate) suggests that mammography plus ultrasound was more sensitive and less specific, and detected more cancers compared to mammography alone. Fewer interval cancers occurred after mammography plus ultrasound (GRADE: Very low to Low), but recall rates were nearly double that of mammography alone (GRADE: Very low to Moderate). Evidence of Low to Very low quality suggested that compared with supplemental DBT, supplemental ultrasound was more sensitive, detected more cancers, and led to more recalls. Among people with extremely dense breasts, fewer interval cancers occurred after mammography plus supplemental MRI compared to mammography alone (GRADE: High). Supplemental MRI after negative mammography was highly accurate in people with extremely dense breasts and heterogeneously dense breasts in nonrandomized and randomized studies (GRADE: Very Low and Moderate). In people with extremely dense breasts, MRI after negative mammography detected 16.5 cancers per 1,000 screens (GRADE: Moderate), and up to 9.5% of all people screened were recalled (GRADE: Moderate). Contrast-related adverse events were infrequent (GRADE: Moderate). No study reported psychological impacts, breast cancer-specific mortality, or overall mortality.We included nine studies in the economic evidence, but none of the study findings was directly applicable to the Ontario context. Our lifetime cost-effectiveness analyses showed that supplemental screening with ultrasound, MRI, or DBT found more screen-detected cancers, decreased the number of interval cancers, had small gains in life-years or quality-adjusted life-years (QALYs), and was associated with savings in cancer management costs. However, supplemental screening also increased imaging costs and the number of false-positive cases. Compared to mammography alone, the incremental cost-effectiveness ratios (ICERs) for supplemental screening with handheld ultrasound, MRI, or DBT for people with dense breasts were $119,943, $314,170, and $212,707 per QALY gained, respectively. The ICERs for people with extremely dense breasts were $83,529, $101,813, and $142,730 per QALY gained, respectively. In sensitivity analyses, the diagnostic test sensitivity of mammography alone and of mammography plus supplemental screening had the greatest effect on ICER estimates. The total budget impact of publicly funding supplemental screening with handheld ultrasound, MRI, or DBT for people with dense breasts over the next 5 years is estimated at $15 million, $41 million, or $33 million, respectively. The corresponding total budget impact for people with extremely dense breasts is $4 million, $10 million, or $9 million.We engaged directly with 70 people via interviews and an online survey. The participants provided diverse perspectives on broad access to supplemental screening for people with dense breasts in Ontario. Themes discussed in the interviews included self-advocacy, patient-doctor partnership, preventive care, and a shared preference for broad access to screening modalities that are clinically effective in detecting breast cancer in people with dense breasts.We included 10 studies in the qualitative evidence rapid review. Thematic synthesis of these reports yielded three analytical themes: coming to know and understand breast density, which included introductions to and making sense of breast density; experiences of vulnerability, which influenced or were influenced by understandings and misunderstandings of breast density and responses to breast density; and choosing supplemental screening, which was influenced by knowledge and perception of the risks and benefits of supplemental screening, and the availability of resources.The ethics review determined that the main harms of supplemental screening for people with dense breasts are false-positives and overdiagnosis, both of which lead to unnecessary and burdensome health care treatments. Screening programs raise inherent tensions between individual- and population-level interests; they may yield population-level benefit, but are statistically of very little benefit to individuals. Entrenched cultural beliefs about the value of breast cancer screening, combined with uncertainty about the effects of supplemental screening on some outcomes and the discomfort of many health care providers in discussing screening options for people with dense breasts suggest that it may be difficult to ensure that patients can provide informed consent to engage in supplemental screening. Funding supplemental screening for people with dense breasts may lead to improved equity in the effectiveness of identifying cancers in people with dense breasts (compared to mammography alone), but it is not clear whether it would lead to equity in terms of improved survival and decreased morbidity. Conclusions: Supplemental screening with ultrasound, DBT, or MRI as an adjunct to mammography detected more cancers and increased the number of recalls and biopsies, including false-positive results. Fewer interval cancers tended to occur after supplemental screening compared to mammography alone. It is unclear whether supplemental screening as an adjunct to mammography would reduce breast cancer-related or overall mortality among people with dense breasts.Supplemental screening with ultrasound, DBT, or MRI as an adjunct to mammography in people aged 50 to 74 years improved cancer detection but increased costs. Depending on the type of imaging modality, publicly funding supplemental screening in Ontario over the next 5 years would require additional total costs between $15 million and $41 million for people with dense breasts, and between $4 million and $10 million for people with extremely dense breasts.The people we engaged with directly valued the potential clinical benefits of supplemental screening and emphasized that patient education and equitable access should be a requirement for implementation in Ontario. Our review of the qualitative literature found that the concept of breast density is poorly understood, both by people with dense breasts and by some general practitioners. People with dense breasts who receive routine mammography (especially those who receive health care in their nonpreferred language or are perceived to have lower economic status or health literacy) and their general practitioners may not have the awareness or knowledge to make informed decisions about supplemental screening. Some people with dense breasts experienced emotional distress from barriers to accessing supplemental screening, and many wanted to engage in supplemental screening, even when educated about its potential harms, including false-positives and overdiagnosis.Given an overall lack of robust evidence about morbidity and mortality associated with supplemental screening for people with dense breasts, it is not possible to determine whether funding supplemental screening for dense breasts delivers on the ethical duties to maximize benefits and minimize harms for populations and individuals. It is likely that existing inequities in access to breast screening and cancer treatment will persist, even if supplemental screening for dense breasts is funded. Continued efforts to address these inequities by removing barriers to screening might mitigate this concern. It will be important to identify and minimize sources of uncertainty related to benefits and risks of supplemental screening for dense breasts to optimize the capacity for everyone involved to live up to their ethical obligations. Some of these may be resolved with further evidence related to the outcomes of supplemental screening for dense breasts.

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)0.032
metaresearch head score (Gemma)0.144
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.048
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.144
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.012
Bibliometrics0.0130.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.125
GPT teacher head0.426
Teacher spread0.300 · 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 designSystematic review
Domainnot available
GenreReview

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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Citations7
Published2023
Admission routes1
Has abstractyes

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