Preoperative MRI features predict failed breast-conserving surgery: construction of a predictive model

Yu-Hong Qu; Ying-Jian He; Xiao-Ting Li; Zhao-Qing Fan; Rui-Jia Sun; Xing Wang; Tao Ouyang; Ying-Shi Sun

doi:10.21037/tcr-21-1919

Preoperative MRI features predict failed breast-conserving surgery: construction of a predictive model

Transl Cancer Res. 2022 Apr;11(4):639-648. doi: 10.21037/tcr-21-1919.

Authors

Yu-Hong Qu^#^{1

2}, Ying-Jian He^#³, Xiao-Ting Li¹, Zhao-Qing Fan³, Rui-Jia Sun¹, Xing Wang³, Tao Ouyang³, Ying-Shi Sun¹

Affiliations

¹ Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China.
² Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
³ Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Department of Breast Center, Peking University Cancer Hospital & Institute, Beijing, China.

^# Contributed equally.

Abstract

Background: Breast-conserving surgery (BCS) is the preferred method for early breast cancer, and the accurate preoperative prediction of the feasibility of BCS can formulate the surgical plan and reduce the violation of the patient's will. The present study proposed to explore the preoperative magnetic resonance imaging (MRI) features associated with failed BCS and constructed an MRI-based model to predict BCS.

Methods: This retrospective study included patients between March 2015 and July 2016, who planned to undergo BCS, had preoperative MRI examination, and had at least 2 years of follow-up. A total of 30 patients with failed BCS were identified and matched with 90 patients with successful BCS (ratio 1:3) according to age, neoadjuvant therapy, and hormone receptor expression. The patients were divided into the training group for model construction and the testing group for model validation. The MRI features, including the site of the tumor, the lesion type, and the lesion and breast volume, were compared between failure and successful BCS groups. A multivariate logistic model for predicting failed BCS was constructed using independent factors associated with failed BCS from the training group and was evaluated in the testing group. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve.

Results: The mean age of the cohort was 45.7±10.3 years. A significantly more non-mass lesion and multifocality, the larger volume of lesion, and the ratio of lesion and breast volume were observed in failed BCS group compared to the successful BCS group. The ratio of lesion and breast volume and multifocality were independent factors associated with failed BCS, odds ratios were 1.044 (95% CI: 1.016-1.074) and 11.161 (95% CI: 1.739-71.652), respectively. An MRI-based model for predicting failed BCS was established, the area under the ROC curves in the training and testing group were 0.902 and 0.821, respectively.

Conclusions: This model might help clinicians predict failed BCS preoperatively and make an accurate surgical strategy.

Keywords: Breast neoplasm; breast-conserving surgery (BCS); magnetic resonance imaging (MRI); mastectomy; segmental; tumor burden.