title: Breast cancer molecular subtype classifier that incorporates MRI features.
abstract: To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes.
  
  
  
  This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test.
  
  
  
  Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR-/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR-/HER2+), and 81.0% (TN).
  
  
  
  We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129.
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Elizabeth J
authors.initials: EJ
authors.lastname: Sutton
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.|Weill Cornell Medical College, Cornell University, New York, New York, USA.
authors.forename: Brittany Z
authors.initials: BZ
authors.lastname: Dashevsky
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Jung Hun
authors.initials: JH
authors.lastname: Oh
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Harini
authors.initials: H
authors.lastname: Veeraraghavan
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Aditya P
authors.initials: AP
authors.lastname: Apte
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Sunitha B
authors.initials: SB
authors.lastname: Thakur
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Morris
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.|Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Joseph O
authors.initials: JO
authors.lastname: Deasy
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging* / Q000378:metabolism*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D064726:Triple Negative Breast Neoplasms / Q000000981:diagnostic imaging
chemical_list: D014408:Biomarkers, Tumor
keywords: MRI texture; breast cancer; machine-learning; molecular subtypes