title: Lymph Node Metastasis Status in Breast Carcinoma Can Be Predicted via Image Analysis of Tumor Histology.
abstract: 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.
  
  
  METHODS
  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%.
  
  
  CONCLUSIONS
  These results demonstrate a technique that potentially can be used to supplant existing surgical methods to determine ALN metastasis status, thereby reducing patient morbidity associated with over-treatment.
authors: Mark D
authors: MD
authors: Zarella
authors: David E
authors: DE
authors: Breen
authors: Alimoor
authors: A
authors: Reza
authors: Aladin
authors: A
authors: Milutinovic
authors: Fernando U
authors: FU
authors: Garcia
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000473:pathology*; D005260:Female; D006652:Histological Techniques; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D011237:Predictive Value of Tests