title: Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.
abstract: BACKGROUND
  The objective of this study was to demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage.
  
  
  METHODS
  The authors used a data set of deidentified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. In total, 91 biopsy-proven breast cancers were analyzed from patients who had information available on pathologic stage (stage I, n = 22; stage II, n = 58; stage III, n = 11) and surgically verified lymph node status (negative lymph nodes, n = 46; ≥ 1 positive lymph node, n = 44; no lymph nodes examined, n = 1). Tumors were characterized according to 1) radiologist-measured size and 2) CEIP. Then, models were built that combined 2 CEIPs to predict tumor pathologic stage and lymph node involvement, and the models were evaluated in a leave-1-out, cross-validation analysis with the area under the receiver operating characteristic curve (AUC) as the value of interest.
  
  
  RESULTS
  Tumor size was the most powerful predictor of pathologic stage, but CEIPs that captured biologic behavior also emerged as predictive (eg, stage I and II vs stage III demonstrated an AUC of 0.83). No size measure was successful in the prediction of positive lymph nodes, but adding a CEIP that described tumor "homogeneity" significantly improved discrimination (AUC = 0.62; P = .003) compared with chance.
  
  
  CONCLUSIONS
  The current results indicate that MRI phenotypes have promise for predicting breast cancer pathologic stage and lymph node status. Cancer 2016;122:748-757. © 2015 American Cancer Society.
authors.affiliation: Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
authors.affiliation: Department of Radiology, University of Chicago, Chicago, Illinois.
authors.forename: Karen
authors.initials: K
authors.lastname: Drukker
authors.affiliation: Department of Radiology, University of Chicago, Chicago, Illinois.
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Radiology, Roswell Park Cancer Institute, Buffalo, New York.
authors.forename: Ermelinda
authors.initials: E
authors.lastname: Bonaccio
authors.affiliation: Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Margarita
authors.initials: M
authors.lastname: Zuley
authors.affiliation: Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Marie
authors.initials: M
authors.lastname: Ganott
authors.affiliation: University of Miami, Miller School of Medicine, Miami, Florida.
authors.forename: Jose M
authors.initials: JM
authors.lastname: Net
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
authors.forename: Elizabeth J
authors.initials: EJ
authors.lastname: Sutton
authors.affiliation: Department of Diagnostic Radiology, Mayo Clinic, Rochester, Minnesota.
authors.forename: Kathleen R
authors.initials: KR
authors.lastname: Brandt
authors.affiliation: Department of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas.
authors.forename: Gary J
authors.initials: GJ
authors.lastname: Whitman
authors.affiliation: Department of Radiology, University of Chicago, Chicago, Illinois.
authors.forename: Suzanne D
authors.initials: SD
authors.lastname: Conzen
authors.affiliation: Department of Radiology, University of Chicago, Chicago, Illinois.
authors.forename: Li
authors.initials: L
authors.lastname: Lan
authors.affiliation: Department of Health Studies, University of Chicago, Chicago, Illinois.|Program of Computational Genomics and Medicine, NorthShore University HealthSystem, Evanston, Illinois.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
authors.affiliation: Program of Computational Genomics and Medicine, NorthShore University HealthSystem, Evanston, Illinois.
authors.forename: Yitan
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: National Cancer Institute, National Institutes of Health, Rockville, Maryland.
authors.forename: Carl C
authors.initials: CC
authors.lastname: Jaffe
authors.affiliation: National Cancer Institute, National Institutes of Health, Rockville, Maryland.
authors.forename: Erich P
authors.initials: EP
authors.lastname: Huang
authors.affiliation: National Cancer Institute, National Institutes of Health, Rockville, Maryland.
authors.forename: John B
authors.initials: JB
authors.lastname: Freymann
authors.affiliation: National Cancer Institute, National Institutes of Health, Rockville, Maryland.
authors.forename: Justin S
authors.initials: JS
authors.lastname: Kirby
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Morris
authors.affiliation: Department of Radiology, University of Chicago, Chicago, Illinois.
authors.forename: Maryellen L
authors.initials: ML
authors.lastname: Giger
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000473:pathology*; D018270:Carcinoma, Ductal, Breast / Q000473:pathology*; D018275:Carcinoma, Lobular / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008198:Lymph Nodes / Q000473:pathology*; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D009367:Neoplasm Staging; D010641:Phenotype; D011379:Prognosis; D012372:ROC Curve
keywords: breast cancer stage; magnetic resonance imaging (MRI); prognosis; quantitative image analysis