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Daqu Zhang, picture taken outdoors. Photo.

Daqu Zhang

Doctoral student

Daqu Zhang, picture taken outdoors. Photo.

Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics

Author

  • Daqu Zhang
  • Miriam Svensson
  • Patrik Edén
  • Looket Dihge

Summary, in English

The axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression, with Transformer showing the strongest results, under the constraint that the sensitivity is no less than 90%, reflecting the sensitivity of SLNB. This highlights the feasibility of noninvasive macro-SLNM prediction using DL. Feature importance analysis revealed that patients with similar characteristics exhibited different nodal status predictions, indicating the need for additional predictors for further improvement.

Department/s

  • Centre for Environmental and Climate Science (CEC)
  • Computational Science for Health and Environment
  • Breast cancer treatment
  • LUCC: Lund University Cancer Centre
  • Breast Cancer Surgery

Publishing year

2024-11-06

Language

English

Publication/Series

Scientific Reports

Volume

14

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Cancer and Oncology

Keywords

  • Breast cancer
  • Lymphatic metastasis
  • Sentinel lymph node
  • Deep learning
  • Clinical decision support

Status

Published

Project

  • Applications of Deep Learning in Breast Cancer Research

Research group

  • Computational Science for Health and Environment
  • Breast Cancer Surgery

ISBN/ISSN/Other

  • ISSN: 2045-2322