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A preoperative predictive model for margin status in breast-conserving surgery
Xinyu Liu1, Yan Liu1, Shichao Zhang1
1The Third Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China; Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China; Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
This study developed a predictive model to identify patients at high risk for positive margins during breast-conserving surgery (BCS). This tool helps reduce re-excision rates and improve local recurrence outcomes.
Area of Science:
- Oncology
- Radiology
- Surgical Oncology
Background:
- Positive margins after breast-conserving surgery (BCS) are a significant risk factor for local recurrence.
- Re-excision is frequently required when positive margins are detected post-BCS.
Purpose of the Study:
- To identify preoperative predictors of positive margins in BCS.
- To establish a predictive model for positive margins in BCS.
Main Methods:
- Retrospective analysis of 2837 primary breast cancer patients undergoing BCS.
- Utilized preoperative imaging: ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI).
- Developed a nomogram using logistic regression in a training cohort and validated it.
Main Results:
- The positive margin rate was 18.6%.
- A predictive model incorporating histological type, MRI parameters (lesion size, FGT, BPE, NME), multifocality, and ALNM was developed.
- The model demonstrated good discrimination with C-indices of 0.782 and 0.761 in the training and validation groups, respectively.
Conclusions:
- A validated preoperative nomogram can predict the risk of positive margins in BCS.
- The nomogram integrates key clinicopathological and imaging parameters for risk assessment.
