Lymph node metastasis status in breast carcinoma can be predicted via image analysis of tumor histology

Mark D. Zarella, David E. Breen, Md Alimoor Reza, Aladin Milutinovic, Fernando U. Garcia

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


OBJECTIVE: To develop a method whereby axillary lymph node (ALN) metastasis can be predicted without ALN dissection, via computational image analysis of routinely acquired tumor histology. STUDY DESIGN: We employed digital image processing to stratify patients based on the histological attributes of the primary tumor. We extracted image features that capture the nuclear and architectural properties of the specimen. We then used a novel machine learning algorithm to transform image features into a scalar score that provided not only a metastasis prediction but also the certainty of classification. RESULTS: We applied this procedure to 101 patients with a ground truth established by histological examination of the lymph nodes and found that 68.3% of the cohort could be classified, exhibiting a correct prediction rate of 88.4%. CONCLUSION: These results demonstrate a technique that potentially can be used to supplant existing surgical methods to determine ALN metastasis status, there-by reducing patient morbidity associated with overtreatment.

Original languageEnglish (US)
Pages (from-to)273-285
Number of pages13
JournalAnalytical and Quantitative Cytology and Histology
Issue number5
StatePublished - 2015
Externally publishedYes


  • Computer-assisted image processing
  • Diagnostic imaging
  • Digital pathology
  • Image processing
  • Informatics
  • Lymph nodes
  • Machine learning
  • Metastasis
  • Morphometry
  • Neoplasm metastasis

ASJC Scopus subject areas

  • Anatomy
  • Histology


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