Predicting complications in breast reconstruction: External validation of a machine learning model.

Journal: Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Published Date: Jun 25, 2025

Abstract

BACKGROUND: Nipple-sparing mastectomy (NSM) with immediate implant-based breast reconstruction provides aesthetic and psychosocial benefits, but nipple-areolar complex (NAC) necrosis remains a significant risk. This study externally validated a previously developed machine learning (ML) model that predicted NAC necrosis with 97% accuracy on institutional data.

Authors

  • Anne M Meyer
    University of Kansas, Department of Plastic and Reconstructive Surgery, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA.
  • Hyung Bae Kim
    ASAN Medical Center, 88 Olympic-ro 43-gil, Songpa District, Seoul, South Korea.
  • Jin Sup Eom
    From the Department of Plastic and Reconstructive Surgery, Asan Medical Center, University of Ulsan College of Medicine.
  • Lauren Sinik
    University of Kansas, Department of Plastic and Reconstructive Surgery, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA.
  • Sterling Braun
    University of Kansas, Department of Plastic and Reconstructive Surgery, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA; ASAN Medical Center, 88 Olympic-ro 43-gil, Songpa District, Seoul, South Korea. Electronic address: Sbraun2@kumc.edu.
  • Hyun Ho Han
    From the Department of Plastic and Reconstructive Surgery, Asan Medical Center, University of Ulsan College of Medicine.
  • James A Butterworth
    University of Kansas, Department of Plastic and Reconstructive Surgery, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA.

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