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Associate Professor Sophia Zackrisson, MD, PhD. Photo.

Sophia Zackrisson

Manager

Associate Professor Sophia Zackrisson, MD, PhD. Photo.

Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer

Author

  • Daqu Zhang
  • Looket Dihge
  • Pär-Ola Bendahl
  • Ida Arvidsson
  • Magnus Dustler
  • Julia Ellbrant
  • Kim Gulis
  • Malin Hjärtström
  • Mattias Ohlsson
  • Cornelia Rejmer
  • David Schmidt
  • Sophia Zackrisson
  • Patrik Edén
  • Lisa Rydén
Show all

Summary, in English

With the shift toward de-escalating surgery in breast cancer, prediction models incorporating imaging can reassess the need for surgical axillary staging. This study employed advancements in deep learning to comprehensively evaluate routine mammograms for preoperative lymph node metastasis prediction. Mammograms and clinicopathological data from 1265 cN0 T1-T2 breast cancer patients (primary surgery, no neoadjuvant therapy) were retrospectively collected from three Swedish institutions. Compared to models using only clinical variables, incorporating full-breast mammograms with preoperative clinical variables improved the ROC AUC from 0.690 to 0.774 (improvement: 0.001-0.154) in the independent test set. The combined model showed good calibration and, at sensitivity ≥90%, achieved a significantly better net benefit, and a sentinel lymph node biopsy reduction rate of 41.7% (13.0-62.6%). Our findings suggest that routine mammograms, particularly full-breast images, can enhance preoperative nodal status prediction. They may substitute key predictors such as pathological tumor size and multifocality, aiding patient stratification before surgery.

Department/s

  • Computational Science for Health and Environment
  • Centre for Environmental and Climate Science (CEC)
  • Breast Cancer Surgery
  • The Liquid Biopsy and Tumor Progression in Breast Cancer
  • Personalized Breast Cancer Treatment
  • Pediatric Nephrology
  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö
  • Surgery (Lund)
  • Breast cancer treatment
  • LUCC: Lund University Cancer Centre
  • Anesthesiology and Intensive Care
  • LU Profile Area: Natural and Artificial Cognition
  • eSSENCE: The e-Science Collaboration
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • LU Profile Area: Light and Materials
  • LTH Profile Area: Photon Science and Technology
  • EpiHealth: Epidemiology for Health

Publishing year

2025-07-10

Language

English

Publication/Series

npj Digital Medicine

Volume

8

Issue

1

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Radiology and Medical Imaging
  • Cancer and Oncology

Status

Published

Research group

  • Computational Science for Health and Environment
  • Breast Cancer Surgery
  • The Liquid Biopsy and Tumor Progression in Breast Cancer
  • Personalized Breast Cancer Treatment
  • Pediatric Nephrology
  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

ISBN/ISSN/Other

  • ISSN: 2398-6352