Abstract #1650

Multiparametric MR-based Feature Fusion Radiomics Combined with ADC Maps-based Tumor Proliferative Burden in Distinguishing TNBC vs. non-TNBC

Fangrong Liang^1,2, Wanli Zhang^1,2, Jiamin Li^1,2, Yongzhou Xu³, Aaron Zhang³, Xinqing Jiang^1,2, Xin Zhen⁴, and Ruimeng Yang^1,2

¹Department of Radiology, The Second Affifiliated 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, Diffusion/other diffusion imaging techniques, Tumor proliferative burden; Triple negative breast cancer; Multiparametric magnetic resonance imagingTriple negative breast cancer (TNBC) is highly heterogeneous, with poorer prognosis, higher recurrence rates and severe treatment challenges. Accurate preoperative identification of TNBC is helpful for individualized patient management. Based on multiparametric magnetic resonance imaging (mMRI), we employed whole-tumor ADC maps-based radiomics (R_ADC) model, tumor proliferative burden (TPB_ADC) model, mMRI-based feature fusion radiomics (R_FF) model and combinational R_FF-TPB_ADC model to investigate their performance in distinguishing TNBC from non-TNBC. Our results showed that the R_FF-TPB_ADC model outperformed the R_ADC, TPB_ADC, and R_FF models by integrating mMRI radiomics features and TPBs, demonstrating its potential for classification of breast cancer.

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