Abstract #0204

Multiparametric MRI-based radiomics fusion combined with quantitative stratified ADC-defined tumor habitats for TNBC identification

Wanli Zhang^1,2, Fangrong Liang^1,2, Jiamin Li^1,2, Yongzhou Xu³, Xin Zhen⁴, Ruimeng Yang^1,2, and Xinqing Jiang^1,2

¹Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China, ²Department of Radiology, Guangzhou First People's Hospital, Guangzhou, China, ³Philips Healthcare, Guangzhou, China, ⁴School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Synopsis

Keywords: Breast, Cancer, Breast cancer, MRI, Triple-negative breast cancer, Tumor habitat

Motivation: Investigated the role of quantitative stratified apparent diffusion coefficient (ADC)-defined tumor habitats in differentiating triple-negative breast cancer (TNBC) from non-TNBC using a multiparametric MRI (mpMRI)-based feature fusion radiomics (R_FF) approach.

Goal(s): To develop an R_FF-Stratified_ADC model using an R_FF strategy and reveal distinct ADC map–based tumor habitats for distinguishing TNBC.

Approach: R_FF (predominant MRI sequence–based fused features), R_ADC (ADC radiomics features), Stratified_ADC (stratified ADC–defined tumor habitat parameters), and combined R_FF-Stratified_ADC models were constructed to identify TNBC.

Results: Stratified ADC parameters helped evaluate the underlying biological proliferation and cellularity within tumor habitats. The integrated R_FF-Stratified_ADC model was effective and reliable for TNBC identification.

Impact: Stratified ADC–defined tumor habitat parameters derived from whole-tumor ADC maps, along with fused radiomics features from dominant mpMRI sequences (T2WI, DWI, ADC maps, and DCE₂), can serve as potential biomarkers for differentiating TNBC from non-TNBC.

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