title: A new, preoperative, MRI-based scoring system for diagnosing malignant axillary lymph nodes in women evaluated for breast cancer.
abstract: OBJECTIVE
  Malignant axillary lymph nodes are an important predictor for breast cancer recurrence, but invasive dissection or biopsy is required for the diagnosis. We determined whether and how malignant nodes could be diagnosed preoperatively with magnetic resonance (MR) imaging.
  
  
  METHODS
  We obtained MR images of all women evaluated for breast cancer at the Sun Yat-Sen University Cancer Center in 2010 and correlated the image characteristics of each axillary node with the pathologic diagnosis of the same node.
  
  
  RESULTS
  We analyzed 251 nodes (117 benign; 134 malignant) from 136 women (mean age, 44 years; range, 20-67). Mean diameter of the nodes was 18 mm (range, 5-58 mm). With pathologic diagnosis as the reference standard, MRI-based interpretations were 66.4% sensitive, 94% specific, and 79% accurate. Diameter, pathologic type, apparent diffusion coefficient value (ADC, b=500 and 800), time-intensity curve (TIC) type of breast tumors correlated with node metastasis; ADC value (b=500 and 800), TIC type, early enhancement rate, long-axis, short-axis, shape, margin and the location of nodes correlated with node metastasis (P<0.001 for all). Tumor immunohistochemistry results for estrogen receptors, progesterone receptors, c-erbB-2, vascular endothelial growth factor, and Ki67 were not. An MRI-based lymph node scoring system based on these correlations had a specificity of 91%, a sensitivity of 93%, and an area under the ROC curve of 0.95 (P<0.001).
  
  
  CONCLUSIONS
  Metastatic axillary lymph nodes can be accurately diagnosed by MR in women with early breast cancer preoperatively and non-invasively. The scoring system appears to be superior to current methods.
authors.affiliation: Department of Medical Imaging & Interventional Radiology, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China. 61684754@qq.com
authors.forename: Ni
authors.initials: N
authors.lastname: He
authors.forename: Chuanmiao
authors.initials: C
authors.lastname: Xie
authors.forename: Weidong
authors.initials: W
authors.lastname: Wei
authors.forename: Changchuan
authors.initials: C
authors.lastname: Pan
authors.forename: Wenyan
authors.initials: W
authors.lastname: Wang
authors.forename: Ning
authors.initials: N
authors.lastname: Lv
authors.forename: Peihong
authors.initials: P
authors.lastname: Wu
mesh_terms: D000368:Aged; D000465:Algorithms; D001365:Axilla; D001943:Breast Neoplasms / Q000473:pathology* / Q000556:secondary* / Q000601:surgery; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008207:Lymphatic Metastasis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009367:Neoplasm Staging; D011300:Preoperative Care; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D055815:Young Adult