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Portrait of Daniel Förnvik. Photo

Daniel Förnvik

Associate professor

Portrait of Daniel Förnvik. Photo

Preoperative prediction of nodal status using clinical data and artificial intelligence derived mammogram features enabling abstention of sentinel lymph node biopsy in breast cancer

Author

  • Cornelia Rejmer
  • Looket Dihge
  • Pär-Ola Bendahl
  • Daniel Förnvik
  • Magnus Dustler
  • Lisa Rydén

Summary, in English

INTRODUCTION: Patients with clinically node-negative breast cancer have a negative sentinel lymph node status (pN0) in approximately 75% of cases and the necessity of routine surgical nodal staging by sentinel lymph node biopsy (SLNB) has been questioned. Previous prediction models for pN0 have included postoperative variables, thus defeating their purpose to spare patients non-beneficial axillary surgery. We aimed to develop a preoperative prediction model for pN0 and to evaluate the contribution of mammographic breast density and mammogram features derived by artificial intelligence for de-escalation of SLNB.

MATERIALS AND METHODS: This retrospective cohort study included 755 women with primary breast cancer. Mammograms were analyzed by commercially available artificial intelligence and automated systems. The additional predictive value of features was evaluated using logistic regression models including preoperative clinical variables and radiological tumor size. The final model was internally validated using bootstrap and externally validated in a separate cohort. A nomogram for prediction of pN0 was developed. The correlation between pathological tumor size and the preoperative radiological tumor size was calculated.

RESULTS: Radiological tumor size was the strongest predictor of pN0 and included in a preoperative prediction model displaying an area under the curve of 0.68 (95% confidence interval: 0.63-0.72) in internal validation and 0.64 (95% confidence interval: 0.59-0.69) in external validation. Although the addition of mammographic features did not improve discrimination, the prediction model provided a 21% SLNB reduction rate when a false negative rate of 10% was accepted, reflecting the accepted false negative rate of SLNB.

CONCLUSION: This study shows that the preoperatively available radiological tumor size might replace pathological tumor size as a key predictor in a preoperative prediction model for pN0. While the overall performance was not improved by mammographic features, one in five patients could be omitted from axillary surgery by applying the preoperative prediction model for nodal status. The nomogram visualizing the model could support preoperative patient-centered decision-making on the management of the axilla.

Department/s

  • Breast cancer treatment
  • LUCC: Lund University Cancer Centre
  • Surgery (Lund)
  • Breast Cancer Surgery
  • Breastcancer-genetics
  • The Liquid Biopsy and Tumor Progression in Breast Cancer
  • Personalized Breast Cancer Treatment
  • Medical Radiation Physics, Malmö
  • Radiology Diagnostics, Malmö

Publishing year

2024

Language

English

Publication/Series

Frontiers in Oncology

Volume

14

Document type

Journal article

Publisher

Frontiers Media S. A.

Topic

  • Radiology, Nuclear Medicine and Medical Imaging
  • Cancer and Oncology

Status

Published

Research group

  • Breast Cancer Surgery
  • The Liquid Biopsy and Tumor Progression in Breast Cancer
  • Personalized Breast Cancer Treatment
  • Medical Radiation Physics, Malmö
  • Radiology Diagnostics, Malmö

ISBN/ISSN/Other

  • ISSN: 2234-943X