TU‐D‐207B‐05: Intra‐Tumor Partitioning and Texture Analysis of DCE‐MRI Identifies Relevant Tumor Subregions to Predict Early Pathological Response of Breast Cancer to Neoadjuvant Chemotherapy

摘要

Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi‐region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE‐MRI). Methods: In this institution review board‐approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 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 a 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. Results: 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 wash‐out were statistically significant (p 0.05) after correcting for multiple testing, with area under the ROC curve 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). Conclusion: The heterogeneity of the tumor subregion associated with fast wash‐out on DCE‐MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.