English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Objectives: To develop and validate a deep learning (DL)-based primary tumorbiopsy signature for predicting axillary lymph node (ALN) metastasispreoperatively in early breast cancer (EBC) patients with clinically negativeALN.  Methods: A total of 1,058 EBC patients with pathologically confirmed ALNstatus 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 wereextracted from the cancer areas of digitized whole-slide images (WSIs) ofbreast CNB specimens annotated by two pathologists. Accuracy, sensitivity,specificity, receiver operating characteristic (ROC) curves, and areas underthe ROC curve (AUCs) were analyzed to evaluate our model.  Results: The best-performing DL-CNB model with VGG16_BN as the featureextractor 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 calledDL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especiallyfor patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). Theinterpretation of DL-CNB model showed that the top signatures most predictiveof ALN metastasis were characterized by the nucleus features including density($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), andorientation ($p$ = 0.012).  Conclusion: Our study provides a novel DL-based biomarker on primary tumorCNB slides to predict the metastatic status of ALN preoperatively for patientswith EBC. The codes and dataset are available athttps://github.com/bupt-ai-cz/BALNMP

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides