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Foto på Patrik Edén

Patrik Edén

Universitetslektor

Foto på Patrik Edén

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

Författare

  • 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.

Avdelning/ar

  • Centrum för miljö- och klimatvetenskap (CEC)
  • Beräkningsvetenskap för hälsa och miljö
  • Bröstcancerbehandling
  • LUCC: Lunds universitets cancercentrum
  • Bröstcancerkirurgi

Publiceringsår

2024-11-06

Språk

Engelska

Publikation/Tidskrift/Serie

Scientific Reports

Volym

14

Dokumenttyp

Artikel i tidskrift

Förlag

Nature Publishing Group

Ämne

  • Cancer and Oncology

Nyckelord

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

Aktiv

Published

Projekt

  • Applications of Deep Learning in Breast Cancer Research

Forskningsgrupp

  • Computational Science for Health and Environment
  • Breast Cancer Surgery

ISBN/ISSN/Övrigt

  • ISSN: 2045-2322