Evidence map›Paper›PMID 41707479›Full record

ArticleBreast (Edinburgh, Scotland)2026

Late distant recurrence prediction model in premenopausal women with ER-positive/HER 2-negative breast cancer: A multicenter retrospective study.

Dong Seung Shin, Janghee Lee, Eunhye Kang, Dasom Noh, Jong-Ho Cheun, Jun-Hee Lee, Yeongyeong Son, Soong June Bae, Sunyoung Kwon, Han-Byoel Lee and 2 more

Abstract readMulticenter Study
In one paragraph

Article in Breast (Edinburgh, Scotland), 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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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.

Dong Seung ShinDivision of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Republic of Korea.
Janghee LeeDepartment of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Eunhye KangDepartment of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Dasom NohDepartment of Information Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Busan, Republic of Korea.
Jong-Ho CheunDepartment of Surgery, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
Jun-Hee LeeDepartment of Surgery, Soonchunhyang University College of Medicine, Soonchunhyang University Hospital, Seoul, Republic of Korea.
Yeongyeong SonDepartment of Information Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Busan, Republic of Korea.
Soong June BaeDepartment of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Sunyoung KwonDepartment of Information Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Busan, Republic of Korea; School of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Yangsan, Republic of Korea; Center for Artificial Intelligence Research, Pusan National University, Busan, Republic of Korea.
Han-Byoel LeeDepartment of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University, Seoul, Republic of Korea; Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: hblee.md@snu.ac.kr.
Jai Min RyuDivision of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University of Medicine, Seoul, Republic of Korea. Electronic address: jaimin.ryu@samsung.com.
Sung Gwe AhnDepartment of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea; Institute for Breast Cancer Precision Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. Electronic address: asg2004@yuhs.ac.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLate distant recurrence (DR) remains a significant challenge in estrogen receptor (ER)-positive/Human Epidermal Growth Factor Receptor 2 (HER2)-negative breast cancer, especially in premenopausal patients. This study aimed to develop a machine-learning model predicting late DR risk in premenopausal patients and to assess the clinical benefit of extended endocrine therapy (ET) according to risk stratification.

methodsThis retrospective multicenter study included patients aged ≤45 years with ER-positive/HER2-negative breast cancer who underwent surgery between January 2000 and December 2011. This study was designed as a landmark analysis, with the effective baseline set at 5 years after surgery. Eligible patients had five to 10 years of follow-up and received adjuvant ET for at least two years. The primary outcome was late DR, defined as distant metastasis occurring between five and 10 years after surgery.

resultsAmong 1701 included patients (median age, 41 years), late DR occurred in 108 patients (6.3%). A machine-learning model using eight clinicopathologic variables demonstrated strong predictive performance (AUC = 0.78). Patients classified as high-risk by the model exhibited significantly worse late DMFS compared to low-risk patients (HR,7.36; 95% CI,4.43-12.20; P<0.001). Among high-risk patients, those who received extended ET had significantly improved late DMFS compared to those who did not (HR,0.32; 95% CI,0.18-0.55; P < 0.001). In low-risk patients, extended ET was not associated with a statistically significant benefit (HR,0.45; 95% CI,0.16-1.22; P = 0.081).

conclusionThe machine-learning model effectively stratified patients into distinct DR risk groups and highlighted the benefit of extended ET in high-risk patients. This model supports tailored decision-making regarding extended ET in premenopausal patients with ER-positive/HER2-negative breast cancer.

Indexed as

Antineoplastic Agents, HormonalBreast NeoplasmsNeoplasm Recurrence, LocalAdultErb-b2 Receptor Tyrosine KinasesFemaleHumansMachine LearningPrediction AlgorithmsPredictive Learning ModelsPremenopauseReceptors, EstrogenRetrospective StudiesRisk AssessmentAntineoplastic Agents, HormonalERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesReceptors, EstrogenBreast cancerExtended endocrine therapyLate distant recurrenceMachine-learning prediction modelPremenopausal

Identifiers

PMID41707479
PMCPMC12926605

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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.