title: Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.
abstract: To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).
  
  
  
  In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC.
  
  
  
  Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65).
  
  
  
  The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115.
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.
authors.forename: Jia
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.|Department of Electronic Engineering, Tsinghua University, Beijing, China.
authors.forename: Guanghua
authors.initials: G
authors.lastname: Gong
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA. rli2@stanford.edu.|Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA. rli2@stanford.edu.
authors.forename: Ruijiang
authors.initials: R
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy* / Q000473:pathology; D017024:Chemotherapy, Adjuvant / Q000379:methods; D016903:Drug Monitoring / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008875:Middle Aged; D020360:Neoadjuvant Therapy / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents
keywords: DCE-MRI; breast cancer; intratumor partitioning; texture analysis; treatment response prediction