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

Journal: NPJ digital medicine
Published Date: Jul 10, 2025

Abstract

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.

Authors

  • Daqu Zhang
    Division of Computational Science for Health and Environment, Center for Environmental and Climate Science, Lund University, Lund, Sweden.
  • Looket Dihge
    Department of Clinical Sciences Lund, Division of Surgery, Lund University, Lund, Sweden.
  • Pär-Ola Bendahl
    Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund University, Lund, Sweden.
  • Ida Arvidsson
    Centre for Mathematical Sciences, Lund University, Lund, Sweden.
  • Magnus Dustler
    Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden.
  • Julia Ellbrant
    Department of Clinical Sciences, Division of Surgery, Lund University, Skåne University Hospital, Lund, Sweden.
  • Kim Gulis
    Department of Clinical Sciences, Division of Surgery, Lund University, Skåne University Hospital, Lund, Sweden.
  • Malin Hjärtström
    Lund University Cancer Centre, Lund University, Lund, Sweden.
  • Mattias Ohlsson
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  • Cornelia Rejmer
    Department of Clinical Sciences, Division of Surgery, Lund University, Skåne University Hospital, Lund, Sweden.
  • David Schmidt
    Skåne University Hospital, Jan Waldenströms gata 35, 205 02 Malmö, Sweden. Electronic address: david.schmidt@med.lu.se.
  • Sophia Zackrisson
    Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden.
  • Patrik Edén
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  • Lisa Rydén
    Department of Clinical Sciences Lund, Division of Surgery, Lund University, Lund, Sweden. lisa.ryden@med.lu.se.

Keywords

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