Evidence map›Paper›PMID 34994879›Full record

ArticleBreast cancer research and treatment2022

Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic.

Mahasti Saghatchian, Marc Abehsera, Amina Yamgnane, Caroline Geyl, Emilien Gauthier, Valérie Hélin, Matéo Bazire, Laure Villoing-Gaudé, Cécile Reyes, David Gentien and 2 more

Open access · bronzeAbstract read
In one paragraph

Article in Breast cancer research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 14 citations in OpenAlex.

  1. Patient and Physician Perspectives on Using Risk Prediction to Support Breast Cancer Surveillance Decision Making.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 1 country.

Mahasti SaghatchianAmerican Hospital of Paris, Neuilly-sur-Seine, France. mahasti.saghatchian@ahparis.org.ORCID http://orcid.org/0000-0003-4628-7783
Marc AbehseraAmerican Hospital of Paris, Neuilly-sur-Seine, France.
Amina YamgnaneAmerican Hospital of Paris, Neuilly-sur-Seine, France.
Caroline GeylAmerican Hospital of Paris, Neuilly-sur-Seine, France.
Emilien GauthierPredilife, Villejuif, France.
Valérie HélinPredilife, Villejuif, France.
Matéo BazireInstitut Curie, Paris, France.
Laure Villoing-GaudéInstitut Curie, Paris, France.
Cécile ReyesInstitut Curie, Paris, France.
David GentienInstitut Curie, Paris, France.
Lisa GolmardINSERM U830 D.R.U.M. Team, Institut Curie Hospital, Paris-University, Paris, France.
Dominique Stoppa-LyonnetInstitut Curie, Paris, France.
Institut Curie · FRHôpital américain de paris · FRDélégation Ile-de-France Villejuif · FRInserm · FRDélégation Paris 5 · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeA personalized approach to prevention and early detection based on known risk factors should contribute to early diagnosis and treatment of breast cancer. We initiated a risk assessment clinic for all women wishing to undergo an individual breast cancer risk assessment.

methodsWomen underwent a complete breast cancer assessment including a questionnaire, mammogram with evaluation of breast density, collection of saliva sample, consultation with a radiologist, and a breast cancer specialist. Women aged 40 or older, with 0 or 1 first-degree relative with breast cancer diagnosed after the age of 40 were eligible for risk assessment using MammoRisk, a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's clinical data and breast density, with or without polygenic risk scores (PRSs). DNA was extracted from saliva samples for genotyping of 76 single-nucleotide polymorphisms. The individual risk was communicated to the patient, with individualized screening and prevention recommendations.

resultsA total of 290 women underwent breast cancer assessment, among which 196 women (68%) were eligible for risk assessment using MammoRisk (median age 52, range 40-72). When PRS was added to MammoRisk, 40% (n = 78) of patients were assigned a different risk category, with 28% (n = 55) of patients changing from intermediate to moderate or high risk.

conclusionIndividual risk assessment is feasible in the general population. Screening recommendations could be given based on individual risk. The use of PRS changed the risk score and screening recommendations in 40% of women.

Indexed as

Breast NeoplasmsAdultBreast DensityEarly Detection of CancerFeasibility StudiesFemaleHumansMammographyMiddle AgedRisk AssessmentRisk FactorsBreast cancerMammographic densityPolygenic risk scoreRisk assessmentScreening

Identifiers

PMID34994879
PMCPMC8739506
OpenAlexW4205159458

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.