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Abstract #0548

Radiogenomics Analysis of DCE-MRI Textures for Tracking Tumor Response and Uncovering Biological Processes in HER2-Negative Breast Cancer

Xinzhi Teng1, Qingpei Lai1, Xinyu Zhang1, Jiang Zhang1, Weihang Liao1, Chenyang Liu1, Haonan Xiao2, Dejun Zhou1, Dianlin Hu1, Tian Li1, and Jing Cai1
1The Hong Kong Polytechnic University, Hong Kong, Hong Kong, 2Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji Nan, China

Synopsis

Keywords: Breast, fMRI Analysis, tumor response tracking, radio-genomics, DCE-MRI

Motivation: DCE-MRI provides non-invasive insights into vascularization and the extracellular matrix, rendering it particularly suitable for assessing tumor response.

Goal(s): To identify the optimal DCE-MRI timing and texture features for predicting pathologic complete response during neoadjuvant chemotherapy in breast cancer patients.

Approach: DCE-MRI scans were taken before, early, and mid-treatment. A sample size of 488 was used for discovery and 250 for validation. Logistic regression compared predictions using benchmark FTV-based analysis and radiomics-based textures with an additional DCE-MRI scan.

Results: Mid-treatment DCE-MRI, measuring texture entropy of dependence count, is optimal for predicting pCR. This texture is uniquely linked to angiogenesis and TGF-beta signaling.

Impact: DCE-MRI effectively tracks breast tumor response by analyzing vascularization and the extracellular matrix. Specifically, mid-treatment scan measuring texture entropy of dependence count optimally predict pathologic complete response, exhibiting exclusive linkage to biological pathways such as angiogenesis and TGF-beta signaling.

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