Evidence map›Paper›PMID 37949155›Full record

SynthesisJournal of the American College of Radiology : JACR2024

Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.

Cody M Schopf, Ojas A Ramwala, Kathryn P Lowry, Solveig Hofvind, M Luke Marinovich, Nehmat Houssami, Joann G Elmore, Brian N Dontchos, Janie M Lee, Christoph I Lee

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of the American College of Radiology : JACR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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.

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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The growing high-risk population.American journal of surgery · 2026
    Article
  3. Article
  4. Article
  5. Cross-Device Adaptation of Mirai for Mammography-Based Breast Cancer Risk Prediction.medRxiv : the preprint server for health sciences · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Article
  20. Article
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

10 authors.

Cody M SchopfDepartment of Radiology, University of Washington School of Medicine, Seattle, Washington.
Ojas A RamwalaDepartment of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington.
Kathryn P LowryDepartment of Radiology, University of Washington School of Medicine, Seattle, Washington.
Solveig HofvindSection Head of Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
M Luke MarinovichThe Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia.
Nehmat HoussamiThe Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; National Breast Cancer Foundation Chair in Breast Cancer Prevention at the University of Sydney and Coeditor of The Breast.
Joann G ElmoreDavid Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California; Director of UCLA's National Clinician Scholars Program and Editor-in-Chief of Adult Primary Care at Up-To-Date. Electronic address: https://twitter.com/JoannElmoreMD.
Brian N DontchosDepartment of Radiology, University of Washington School of Medicine, Seattle, Washington; Clinical Director of Breast Imaging at Fred Hutchinson Cancer Center.
Janie M LeeSection Chief of Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Director of Breast Imaging at Fred Hutchinson Cancer Center.
Christoph I LeeDepartment of Radiology, University of Washington School of Medicine, Seattle, Washington, and Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, WA; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of Journal of the American College of Radiology. Electronic address: stophlee@uw.edu.

Funding

Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARLA M KERLIKOWSKE · 2011 to 2026
$52.9M
Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical TranslationR37CA240403 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI CHRISTOPH I LEE · 2020 to 2026
$3.8M
Population-Based Evaluation of Artificial Intelligence for Mammography Priorto Widespread Clinical TranslationR01CA262023 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI CHRISTOPH I LEE · 2022 to 2026
$3.2M
NCI NIH HHS P01 CA154292NCI NIH HHS R01 CA262023NCI NIH HHS R37 CA240403
6 · The paper itself

Abstract

purposeTo summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction. MATERIALS AND

methodsA systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC).

resultsSixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement.

conclusionsEarly efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.

Indexed as

Artificial IntelligenceBreast NeoplasmsMammographyAlgorithmsEarly Detection of CancerFemaleHumansPredictive Value of TestsRisk AssessmentRisk FactorsArtificial intelligencerisk predictionscreening mammography

Identifiers

PMID37949155
PMCPMC10926179

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.