Evidence map›Paper›PMID 42902311›Full record

ArticleBreast cancer research : BCR2026

Preoperative prediction of positive margins in breast-conserving surgery using an ensemble model based on DCE-MRI: a multi-center retrospective and prospective study.

Xue Zhao, Jing-Wen Bai, Li-Ang Dong, Zhen-Hui Li, Jie-Zhou He, Yi-Xin Chen, Yong Liu, Wen-Tai Hou, Ze-Yan Xu, Zhi-Cheng Du and 3 more

Abstract readMulticenter Study
In one paragraph

Article in Breast cancer research : BCR, 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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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Xue Zhao *The Breast Center and the Cancer Institute, Yunnan Key Laboratory of Molecular Imaging in Oncology, Yunnan International Joint Laboratory on Cancer Molecular Imaging, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, 519 Kunzhou Rd, Xishan District, Kunming, 650118, China.
Jing-Wen Bai *The Breast Center and the Cancer Institute, Yunnan Key Laboratory of Molecular Imaging in Oncology, Yunnan International Joint Laboratory on Cancer Molecular Imaging, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, 519 Kunzhou Rd, Xishan District, Kunming, 650118, China.
Li-Ang DongSchool of Software, Yunnan University, Kunming, China.
Zhen-Hui LiDepartment of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Jie-Zhou HeDepartment of Artificial Intelligence, Xiamen University, Xiamen, China.
Yi-Xin ChenThe Breast Center and the Cancer Institute, Yunnan Key Laboratory of Molecular Imaging in Oncology, Yunnan International Joint Laboratory on Cancer Molecular Imaging, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, 519 Kunzhou Rd, Xishan District, Kunming, 650118, China.
Yong LiuThe Breast Center and the Cancer Institute, Yunnan Key Laboratory of Molecular Imaging in Oncology, Yunnan International Joint Laboratory on Cancer Molecular Imaging, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, 519 Kunzhou Rd, Xishan District, Kunming, 650118, China.
Wen-Tai HouThe Breast Center and the Cancer Institute, Yunnan Key Laboratory of Molecular Imaging in Oncology, Yunnan International Joint Laboratory on Cancer Molecular Imaging, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, 519 Kunzhou Rd, Xishan District, Kunming, 650118, China.
Ze-Yan XuDepartment of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Zhi-Cheng DuThe Breast Center and the Cancer Institute, Yunnan Key Laboratory of Molecular Imaging in Oncology, Yunnan International Joint Laboratory on Cancer Molecular Imaging, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, 519 Kunzhou Rd, Xishan District, Kunming, 650118, China.
Sen JiangDepartment of Radiology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Shao-Zi LiDepartment of Artificial Intelligence, Xiamen University, Xiamen, China.
Guo-Jun ZhangThe Breast Center and the Cancer Institute, Yunnan Key Laboratory of Molecular Imaging in Oncology, Yunnan International Joint Laboratory on Cancer Molecular Imaging, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, 519 Kunzhou Rd, Xishan District, Kunming, 650118, China. zhangguojun@kmmu.edu.cn.ORCID http://orcid.org/0000-0001-5182-5887

Funding

horizontal project funding from the Third Affiliated Hospital of Kunming Medical University 20233160A0866horizontal project funding from the Third Affiliated Hospital of Kunming Medical University 20243160A0511Key Research and Development Program for Social Development of Yunnan Science and Technology Department 202403AC100014National Natural Science Foundation of China 32171363National Natural Science Foundation of China 82103614Scientific Research Project of Yunnan Clinical Medicine Center 2024YNLCYXZX0418start-up and supporting Funds from the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital GCCJJ-4-2024001start-up and supporting Funds from the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital ZC0024unnan Science and Technology Talent and Platform Program 202505AF350067
6 · The paper itself

Abstract

backgroundBreast-conserving surgery (BCS) is the standard care for early breast cancer. However, positive surgical margins lead to high recurrence and require reoperation, remaining a clinical challenge. This study aims to develop an ensemble model to preoperatively predict positive margins using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

methodsA total of 887 patients from three Chinese medical centers were included. Patients from the Yunnan (581 patients) and Fujian (51 patients) provinces were used for model development, with external testing on 188 patients from Guangdong and prospective testing on 67 patients from Yunnan. Three base models were developed: the clinical model based on 32 features, the deep learning (DL) model using ResNet-34, and the radiomics model utilizing radiomics features. The ensemble model combined predictions from these three base models using weighted voting. Performance metrics (accuracy, AUC, sensitivity, specificity) were computed, and prognostic analyses were conducted with a public dataset.

resultsThe ensemble model outperformed each of the three base models, with the AUC of 0.931, accuracy of 0.872, sensitivity of 0.871, and specificity of 0.880 in the validation set. Robustness was further confirmed in independent test cohorts, yielding AUCs of 0.762 and 0.861. Furthermore, patients identified at high-risk by the model exhibited poor recurrence-free survival (RFS, HR = 8.117, p < 0.0001) and overall survival (OS, HR = 5.748, p < 0.0001), underscoring its prognostic value.

conclusionsThe ensemble model based on DCE-MRI effectively predicts positive margins and prognosis for BCS patients, facilitating the clinical decision-making on the surgery. Trial registration This study was approved by the Ethics Committee of the Yunnan Cancer Hospital (KYLX2023-134), Xiang'an Hospital of Xiamen University (XDYX202305K26), and Cancer Hospital of Shantou University Medical College (2024059). The study was also registered with the Chinese Clinical Trial Registry (ChiCTR2400083298, Registered 19 April 2024, https://www.chictr.org.cn/showproj.html?%20proj=207948 ).

Indexed as

Breast NeoplasmsMargins of ExcisionMastectomy, SegmentalAdultDynamic Contrast Enhanced Magnetic Resonance ImagingEnsemble LearningFemaleHumansMiddle AgedPredictive Learning ModelsPrognosisProspective StudiesRadiomicsRetrospective StudiesBreast-conserving surgeryDeep learningDynamic contrast-enhanced magnetic resonance imagingEnsemble modelPositive surgical marginsSurvival

Identifiers

PMID42902311
PMCPMC13685125

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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.