Using quantitative image analysis to classify axillary lymph nodes on breast MRI: A new application for the Z 0011 Era

David V Schacht*, Karen Drukker, Iris Pak, Hiroyuki Abe, Maryellen L. Giger

*Corresponding author for this work

Research output: Contribution to journalArticle

16 Scopus citations


Purpose To assess the performance of computer extracted feature analysis of dynamic contrast enhanced (DCE) magnetic resonance images (MRI) of axillary lymph nodes. To determine which quantitative features best predict nodal metastasis. Methods This institutional board-approved HIPAA compliant study, in which informed patient consent was waived, collected enhanced T1 images of the axilla from patients with breast cancer. Lesion segmentation and feature analysis were performed on 192 nodes using a laboratory-developed quantitative image analysis (QIA) workstation. The importance of 28 features were assessed. Classification used the features as input to a neural net classifier in a leave-one-case-out cross-validation and evaluated with receiver operating characteristic (ROC) analysis. Results The area under the ROC curve (AUC) values for features in the task of distinguishing between positive and negative nodes ranged from just over 0.50 to 0.70. Five features yielded AUCs greater than 0.65: two morphological and three textural features. In cross-validation, the neural net classifier obtained an AUC of 0.88 (SE 0.03) for the task of distinguishing between positive and negative nodes. Conclusion QIA of DCE MRI demonstrated promising performance in discriminating between positive and negative axillary nodes.

Original languageEnglish (US)
Pages (from-to)392-397
Number of pages6
JournalEuropean journal of radiology
Issue number3
StatePublished - Mar 1 2015



  • Axilla
  • Breast neoplasms
  • Computer-assisted
  • Image processing
  • ROC curve

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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