Trial reportNature medicine2024
AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.
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.
What it found
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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.
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.
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
Who cites it
30 citing papers in PubMed.
- The lived experiences of social isolation among cancer patients: a systematic review and meta-synthesis.International journal of nursing studies advances · 2026Review
- Ten Years of Artificial Intelligence in Screening Mammography: A Systematic Review and Meta-Analysis of Diagnostic Accuracy and Clinical Implementation (Literature Published 2015-2025).Diagnostics (Basel, Switzerland) · 2026Review
- The role of artificial intelligence in precision medicine for breast cancer.Discover oncology · 2026Review
- Deep learning model for noninvasive prediction of Ki-67 expression and prognostic stratification in breast cancer: a multicenter retrospective study.European radiology · 2026Article
- Review
- Article
- Artificial Intelligence in Early Breast Cancer Detection: A Systematic Review of Innovations in Preventive Women's Healthcare.Healthcare (Basel, Switzerland) · 2026Review
- A Deep Learning Breast Cancer Risk Model for Precise Supplemental Screening.JAMA network open · 2026Article
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Outcomes of Density-Targeted Supplemental Breast Magnetic Resonance Imaging Screening by Breast Cancer Risk: Long-Term Health and Economic Considerations.Annals of internal medicine · 2026Article
- Evolving Concepts and Contemporary Management of Early-Stage Breast Cancer: An Evidence-Based Approach to Grey Zones from a Comprehensive Breast Unit Part 1: Locoregional Therapy, Pathology, Radiology.European journal of breast health · 2026Article
- Performance of breast cancer risk prediction algorithms across mammography systems in the UK screening programme.NPJ digital medicine · 2026Article
- Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.NPJ breast cancer · 2026Article
- Evaluation of the effect of peritumoral surrounding parenchymal features on radiological size measurement in breast cancer patients: a multicenter retrospective study (TR-BRC 2023-01).Japanese journal of radiology · 2026Article
- Retrospective evaluation of interval breast cancer screening mammograms by radiologists and AI.European radiology · 2026Article
- Epidemiology, early detection, and management of breast cancer in China: A comprehensive review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- A novel four-serum marker model for early detection and therapeutic monitoring of breast cancer.Scientific reports · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.NPJ breast cancer · 2025Article
- Survival Benefits and Less Intensive Treatment for Women with Early-Stage Breast Cancer Diagnosed While Participating in Population-Based Screening.Annals of surgical oncology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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 .
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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.