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Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network.

Frederik Abel1, Anna Landsmann1, Patryk Hejduk1

  • 1Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland.

Diagnostics (Basel, Switzerland)
|June 24, 2022
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Summary

A deep convolutional neural network (dCNN) shows high accuracy in detecting abnormal axillary lymph nodes on mammograms. This artificial intelligence tool demonstrates reliable performance comparable to human readers, aiding in breast cancer diagnosis.

Keywords:
artificial intelligenceaxillary lymph nodesbreast cancerdCNNdeep learningmammographymammography screeningsuspicious lymph nodes

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Machine Learning for Diagnostics

Background:

  • Accurate detection of axillary lymph node abnormalities is crucial for breast cancer staging and treatment planning.
  • Mammography is a primary imaging modality for breast cancer screening, but subtle lymph node changes can be challenging to interpret.
  • Deep convolutional neural networks (dCNNs) offer potential for automated image analysis and improved diagnostic accuracy.

Purpose of the Study:

  • To evaluate the feasibility and accuracy of a dCNN for detecting abnormal axillary lymph nodes on mammograms.
  • To compare the performance of the dCNN against human readers in a real-world setting.
  • To assess the reliability of dCNN-based classification of lymph node status.

Main Methods:

  • A retrospective study utilizing 107 mammographic images from 74 patients, categorized into breast tissue, benign lymph nodes, and suspicious lymph nodes.
  • Training and validation of a dCNN model using 5385 preprocessed images.
  • Testing the dCNN on a separate 'real-world' dataset and comparing its performance against radiological reports and human readers, using confusion matrices and intraclass correlation coefficients.

Main Results:

  • The dCNN achieved high accuracy: 98% for training, 99% for validation, and 98.51% (breast tissue), 98.63% (benign lymph nodes), and 95.96% (suspicious lymph nodes) on the 'real-world' dataset.
  • Excellent intraclass correlation (0.98) and nearly perfect Kappa values (0.93-0.97) were observed between the dCNN and human readers.
  • Visualization using colormaps successfully highlighted abnormal lymph nodes with excellent image quality.

Conclusions:

  • Deep convolutional neural networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.
  • The dCNN demonstrates performance comparable to human readers, suggesting its potential as an assistive tool in mammographic interpretation.
  • This proof-of-principle study indicates the significant potential of AI in improving the accuracy and efficiency of breast cancer diagnostics.