Artificial neural network models to predict nodal status in clinically node-negative breast cancer.

Journal: BMC cancer
PMID: 31226956

Abstract

BACKGROUND: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical strategies are applied depending on the extent of nodal involvement. Preoperative prediction of nodal status is thus important for individualizing axillary surgery avoiding unnecessary surgery. We aimed to predict nodal status in clinically node-negative breast cancer and identify candidates for SLNB omission by including patient-related and pathological characteristics into artificial neural network (ANN) models.

Authors

  • Looket Dihge
    Department of Clinical Sciences Lund, Division of Surgery, Lund University, Lund, Sweden.
  • Mattias Ohlsson
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  • Patrik Edén
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  • Pär-Ola Bendahl
    Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund University, Lund, Sweden.
  • Lisa Rydén
    Department of Clinical Sciences Lund, Division of Surgery, Lund University, Lund, Sweden. lisa.ryden@med.lu.se.

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