Evidence map›Paper›PMID 38977914›Full record

Trial reportNature medicine2024

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.

Mattie Salim, Yue Liu, Moein Sorkhei, Dimitra Ntoula, Theodoros Foukakis, Irma Fredriksson, Yanlu Wang, Martin Eklund, Hossein Azizpour, Kevin Smith and 1 more

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04832594 (Image Analysis With Artificial Intelligence to Increase Precision in Breast Cancer Screening - the ScreenTrust MRI Substudy), which is not on this map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
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.

NCT04832594 naunknown statusnot on this map

Image Analysis With Artificial Intelligence to Increase Precision in Breast Cancer Screening - the ScreenTrust MRI Substudy: a Prospective Trial of AI to Select Women for Supplemental Screening MRI

TypeinterventionalSponsorKarolinska University HospitalRan2021 to 2025Enrolled2,500ConditionsBreast CancerArmsAI selection for supplemental breast MRI
3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

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  16. Epidemiology, early detection, and management of breast cancer in China: A comprehensive review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025
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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

11 authors.

Mattie SalimDepartment of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-3239-1803
Yue LiuSchool of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden.
Moein SorkheiSchool of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden.ORCID 0000-0001-6204-0778
Dimitra NtoulaBreast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden.
Theodoros FoukakisDepartment of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0001-8952-9987
Irma FredrikssonDepartment of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Yanlu WangDepartment of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Martin EklundDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0001-5032-5266
Hossein AzizpourDivision of Robotics, Perception, and Learning, Karolinska Institutet, Stockholm, Sweden.
Kevin SmithSchool of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden.
Fredrik StrandDepartment of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. fredrik.strand@ki.se.ORCID 0000-0003-3910-7086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594 .

Indexed as

Artificial IntelligenceBreast NeoplasmsEarly Detection of CancerMagnetic Resonance ImagingMammographyAdultAgedFemaleHumansMass ScreeningMiddle AgedPatient Selection

Identifiers

PMID38977914
PMCPMC11405258

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Registered trials

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.