Evidence map›Paper›PMID 41927979›Full record

ArticleEuropean radiology2026

Long-term prognostic implications of AI-detected versus AI-undetected breast cancers on mammography: a propensity score-matched analysis.

Hee Jeong Kim, Eun Young Chae, Hye Joung Eom, Woo Jung Choi, Hee Jung Shin, Joo Hee Cha, Hak Hee Kim

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hee Jeong KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Eun Young ChaeDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. chaeey@gmail.com.ORCID http://orcid.org/0000-0002-3478-6721
Hye Joung EomDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Woo Jung ChoiDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Hee Jung ShinDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Joo Hee ChaDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Hak Hee KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.

Funding

Lunit A research-purpose server equipped with an AI-based software program (Lunit INSIGHT MMG version 1.1.8.2)
6 · The paper itself

Abstract

objectivesTo evaluate the association between the cancer detectability by artificial intelligence (AI) and long-term survival outcomes in invasive breast cancer. MATERIALS AND

methodsThis retrospective study analyzed consecutive women diagnosed with invasive breast cancer who underwent preoperative mammography between January and December 2013. Mammograms were analyzed using FDA-cleared AI software (Lunit INSIGHT MMG v1.1.8.2). Cancers were classified as AI-detected if correctly localized by AI, and AI-undetected if AI missed or mislocalized. Propensity score matching was performed using 29 clinical, pathological, and treatment-related covariates. Recurrence-free survival (RFS) and overall survival (OS) were compared using Kaplan-Meier estimates and Cox proportional hazards models.

resultsAmong 879 women (mean age ± standard deviation, 50.3 ± 10.2 years), AI correctly identified cancers in 83%. Before matching, the AI-detected group had higher recurrence (11% vs 5%; p = 0.02) and mortality rates (7% vs 1%; p = 0.003). Distant recurrence was also more prevalent in AI-detected cases (p = 0.04). After matching, no differences were observed in RFS (HR, 1.7 [95% CI: 0.8, 3.9]; p = 0.20) or OS (HR, 4.1 [95% CI: 0.5, 38.1]; p = 0.21). AI detectability was not associated with RFS (HR, 1.9 [95% CI: 0.9, 3.8]; p = 0.07) or OS (HR, 5.5 [95% CI: 0.8, 40.7]; p = 0.09) in multivariable analysis.

conclusionAI-detected breast cancers showed higher recurrence and mortality rates in the unadjusted analysis. However, after adjusting for confounders, AI detectability was not associated with RFS or OS, suggesting that AI may preferentially detect tumors with aggressive characteristics. KEY POINTS: Question AI-based software for mammography interpretation is increasingly being integrated into practice, but the long-term prognostic implications of breast cancers detected or undetected by AI remain unclear. Findings In this retrospective study, AI detectability was not associated with recurrence-free (HR, 1.7; p = 0.20) or overall survival (HR, 4.1; p = 0.21) after propensity score matching. Clinical relevance AI may be more likely to detect biologically aggressive tumors, rather than directly influencing survival.

Indexed as

Artificial IntelligenceBreast NeoplasmsMammographyAdultAgedDisease-Free SurvivalFemaleHumansMiddle AgedNeoplasm Recurrence, LocalPrognosisPropensity ScoreProportional Hazards ModelsRetrospective StudiesArtificial intelligenceBreastBreast neoplasmsMammography

Identifiers

PMID41927979

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