Preoperative prediction of positive margins in breast-conserving surgery using an ensemble model based on DCE-MRI: a multi-center retrospective and prospective study
In brief
DCE-MRI ensemble model predicted positive breast surgery margins with 0.93 AUC
In a study of 887 patients, the model's AUC reached 0.931 in validation and 0.762 and 0.861 in two independent test cohorts. It also identified patients with poorer survival, but the study does not show whether using the model to guide surgery reduces reoperations or improves outcomes.
- Journal
- Breast cancer research : BCR (Q1)
- Published
- 25 July 2026
- Study design
- Prospective / inception cohort
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Xue Zhao, Jing-Wen Bai, Li-Ang Dong, Zhen-Hui Li, Jie-Zhou He, Yi-Xin Chen, et al.
- PMID
- 42902311
- DOI
- 10.1186/s13058-026-02354-0
Why clinicians should know about it
- Picked for Breast and Endocrine Surgery (paper of the day, 22 October 2026): Predicts positive margins and re-excision risk after breast-conserving surgery
Abstract
BACKGROUND: Breast-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). METHODS: A 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. RESULTS: The 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. CONCLUSIONS: The 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 ).
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.