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Oncologic Outcomes in Nipple-sparing Mastectomy with Immediate Reconstruction and Total Mastectomy with Immediate Reconstruction in Women with Breast Cancer: A Machine-Learning Analysis

  • Global Health Services Research
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

This study used a single-institution cohort, the Severance dataset, validated the results by using the surveillance, epidemiology, and end results (SEER) database, adjusted with propensity-score matching (PSM), and analyzed by using a machine learning method.

To determine whether the 5-year, disease-free survival (DFS) and overall survival (OS) of patients undergoing nipple-sparing mastectomy (NSM) with immediate breast reconstruction (IBR) are not inferior to those of women treated with total mastectomy/skin-sparing mastectomy (TM/SSM).

Methods

The Severance dataset enrolled 611 patients with early, invasive breast cancer from 2010 to 2017. The SEER dataset contained data for 485,245 patients undergoing TM and 14,770 patients undergoing NSM between 2000 and 2018. All patients underwent mastectomy and IBR. Intraoperative, frozen-section biopsy for the retro-areolar tissue was performed in the NSM group. The SEER dataset was extracted by using operation types, including TM/SSM and NSM. The primary outcome was DFS for the Severance dataset and OS for the SEER dataset. PSM analysis was applied. Survival outcomes were analyzed by using the Kaplan-Meier method and Cox proportional hazard (Cox PH) regression model. We implemented XGBSE to predict mortality with high accuracy and evaluated model prediction performance using a concordance index. The final model inspected the impact of relevant predictors on the model output using shapley additive explanation (SHAP) values.

Results

In the Severance dataset, 151 patients underwent NSM with IBR and 460 patients underwent TM/SSM with IBR. No significant differences were found between the groups. In multivariate analysis, NSM was not associated with reduced oncologic outcomes. The same results were observed in PSM analysis. In the SEER dataset, according to the SHAP values, the individual feature contribution suggested that AJCC stage ranks first. Analyses from the two datasets confirmed no impact on survival outcomes from the two surgical methods.

Conclusions

NSM with IBR is a safe and feasible procedure in terms of oncologic outcomes. Analysis using machine learning methods can be successfully applied to identify significant risk factors for oncologic outcomes.

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Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1C1C1010441) and a faculty research grant of Yonsei University College of Medicine (6-2021-0239).

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Author notes

  1. Jun-Ho Cho and Jung Mi Park have contributed equally to this work.

Authors and Affiliations

  1. Department of Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jun-Ho Cho MD, Hyung Seok Park MD, PhD, Hye Jin Kim MD, Dong-Min Shin MD, PhD, Jee Ye Kim MD, PhD, Seho Park MD, PhD, Seung Il Kim MD, PhD & Byeong-Woo Park MD, PhD

  2. Department of Biostatistics and Computing, Graduate School, Yonsei University, Seoul, Korea

    Jung Mi Park MS

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Cho, JH., Park, J.M., Park, H.S. et al. Oncologic Outcomes in Nipple-sparing Mastectomy with Immediate Reconstruction and Total Mastectomy with Immediate Reconstruction in Women with Breast Cancer: A Machine-Learning Analysis. Ann Surg Oncol 30, 7281–7290 (2023). https://doi.org/10.1245/s10434-023-13963-w

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