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Abstract #2169

Characterizing Tumor Habitats in Breast Cancer: A DKI Approach to Predicting Estrogen Receptor Expression

Haifa Liu1, Qi Wang1, Ziyi Mao1, Hui Liu1, Mengzhu Wang2, Yang Song2, Chengxiu zhang3, and Guang Yang3
1Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China, 2MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China, 3East China Normal University, Shanghai, China

Synopsis

Keywords: DWI/DTI/DKI, Radiomics, estrogen receptor; tumor habitats

Motivation: This study assessed estrogen receptor (ER) expression using a non-invasive method based on diffusion kurtosis imaging (DKI) habitat analysis to reveal tumor microenvironment characteristics.

Goal(s): We aimed to investigate the relationship between tumor habitats and ER expression, focusing on microstructural complexity and water diffusion.

Approach: The predictive model employed segmentation and unsupervised clustering of DKI data, highlighting spatial variations within tumor subregions.

Results: We identified four distinct tumor subregions, with significant differences in mean kurtosis (MK) and mean diffusivity (MD), indicating that the model effectively predicted ER expression based on microenvironment heterogeneity.

Impact: This study demonstrates that diffusion kurtosis imaging can non-invasively predict estrogen receptor expression by characterizing tumor microenvironments, revealing significant relationships between tumor habitats and their microstructural properties.

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