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Area of Science:

  • Oncology
  • Surgical Pathology
  • Biostatistics

Background:

  • Breast cancer (BC) molecular subtypes correlate with axillary lymph node (LN) status.
  • Accurate prediction of LN positivity is essential for guiding treatment strategies.

Purpose of the Study:

  • To enhance multivariable models for predicting LN metastases.
  • To develop nomograms using logistic regression with clinical and pathological variables, from surgical results or biopsy data.

Main Methods:

  • A retrospective cohort of 12,572 early BC patients was divided into training and validation sets.
  • Multivariable logistic regression was used to build predictive nomograms for LN metastases risk.
  • Model performance was evaluated using discrimination (AUC) and calibration on both sets.

Main Results:

  • Pathologic and pre-operative models achieved AUCs of 0.780 and 0.717 (training) and 0.796 and 0.725 (validation), respectively.
  • Age, tumor size, lymphovascular invasion (LVI), and molecular subtype were significant predictors of LN metastases.
  • The models demonstrated good calibration accuracy.

Conclusions:

  • Proposed nomograms effectively predict the risk of sentinel lymph node (SLN) and non-sentinel node (NSN) involvement.
  • These models can inform decisions on axillary LN staging and the need for axillary lymph node dissection (ALND).
  • They also assist in planning immediate breast reconstruction for patients without LN metastasis requiring radiotherapy.