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

Zhang, Daqu LU ; Dihge, Looket LU ; Bendahl, Pär-Ola LU ; Arvidsson, Ida LU ; Dustler, Magnus LU orcid ; Ellbrant, Julia LU ; Gulis, Kim LU orcid ; Hjärtström, Malin LU orcid ; Ohlsson, Mattias LU orcid and Rejmer, Cornelia LU orcid , et al. (2025) In npj Digital Medicine 8(1).
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
npj Digital Medicine
volume
8
issue
1
article number
425
publisher
Nature Publishing Group
external identifiers
  • scopus:105010427049
  • pmid:40640522
ISSN
2398-6352
DOI
10.1038/s41746-025-01831-8
language
English
LU publication?
yes
additional info
© 2025. The Author(s).
id
e71c7270-ee0e-45a4-bc17-ce5663875dfa
date added to LUP
2025-07-23 13:35:30
date last changed
2025-07-24 04:05:58
@article{e71c7270-ee0e-45a4-bc17-ce5663875dfa,
  abstract     = {{<p>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.</p>}},
  author       = {{Zhang, Daqu and Dihge, Looket and Bendahl, Pär-Ola and Arvidsson, Ida and Dustler, Magnus and Ellbrant, Julia and Gulis, Kim and Hjärtström, Malin and Ohlsson, Mattias and Rejmer, Cornelia and Schmidt, David and Zackrisson, Sophia and Edén, Patrik and Rydén, Lisa}},
  issn         = {{2398-6352}},
  language     = {{eng}},
  month        = {{07}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{npj Digital Medicine}},
  title        = {{Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer}},
  url          = {{http://dx.doi.org/10.1038/s41746-025-01831-8}},
  doi          = {{10.1038/s41746-025-01831-8}},
  volume       = {{8}},
  year         = {{2025}},
}