title: Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer.
abstract: OBJECTIVE
  To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI.
  
  
  METHODS
  60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM). All features except for patterns of response were derived before chemotherapy; GLCM features were determined before and after chemotherapy. Treatment response was defined by the presence of residual invasive tumor and/or positive lymph nodes after chemotherapy. Statistical modeling was performed using Lasso logistic regression.
  
  
  RESULTS
  Pre-chemotherapy imaging features predicted all measures of response except for residual tumor. Feature sets varied in effectiveness at predicting different definitions of treatment response, but in general, pre-chemotherapy imaging features were able to predict pathological complete response with area under the curve (AUC)=0.68, residual lymph node metastases with AUC=0.84 and residual tumor with lymph node metastases with AUC=0.83. Imaging features assessed after chemotherapy yielded significantly improved model performance over those assessed before chemotherapy for predicting residual tumor, but no other outcomes.
  
  
  CONCLUSIONS
  DCE-MRI features can be used to predict whether triple-negative breast cancer patients will respond to NAC. Models such as the ones presented could help to identify patients not likely to respond to treatment and to direct them towards alternative therapies.
authors.affiliation: Department of Radiology, Stanford University Medical Center, Stanford, California, USA.
authors.forename: Daniel I
authors.initials: DI
authors.lastname: Golden
authors.forename: Jafi A
authors.initials: JA
authors.lastname: Lipson
authors.forename: Melinda L
authors.initials: ML
authors.lastname: Telli
authors.forename: James M
authors.initials: JM
authors.lastname: Ford
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D000328:Adult; D000970:Antineoplastic Agents / Q000627:therapeutic use; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000032:analysis*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D020360:Neoadjuvant Therapy; D064726:Triple Negative Breast Neoplasms / Q000737:chemistry / Q000188:drug therapy* / Q000473:pathology*
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor
keywords: BI-RADS; Biomedical imaging Informatics; Heterogeneity; Pharmacokinetics; Treatment response; Triple-Negative breast cancer