  Objectives: To develop and validate a deep learning (DL)-based primary tumor
biopsy signature for predicting axillary lymph node (ALN) metastasis
preoperatively in early breast cancer (EBC) patients with clinically negative
ALN.
  Methods: A total of 1,058 EBC patients with pathologically confirmed ALN
status were enrolled from May 2010 to August 2020. A DL core-needle biopsy
(DL-CNB) model was built on the attention-based multiple instance-learning
(AMIL) framework to predict ALN status utilizing the DL features, which were
extracted from the cancer areas of digitized whole-slide images (WSIs) of
breast CNB specimens annotated by two pathologists. Accuracy, sensitivity,
specificity, receiver operating characteristic (ROC) curves, and areas under
the ROC curve (AUCs) were analyzed to evaluate our model.
  Results: The best-performing DL-CNB model with VGG16_BN as the feature
extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865)
in predicting positive ALN metastasis in the independent test cohort.
Furthermore, our model incorporating the clinical data, which was called
DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially
for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The
interpretation of DL-CNB model showed that the top signatures most predictive
of ALN metastasis were characterized by the nucleus features including density
($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and
orientation ($p$ = 0.012).
  Conclusion: Our study provides a novel DL-based biomarker on primary tumor
CNB slides to predict the metastatic status of ALN preoperatively for patients
with EBC. The codes and dataset are available at
https://github.com/bupt-ai-cz/BALNMP

Image and Video Processing;Computer Vision and Pattern Recognition;Medical Physics