Abstract #4976

Evaluate Values of First-order Features based on Advanced DWI Models in Predicting the HER-2 Expression in Breast Invasive Ductal Cancer

Siyao Du¹, Mengfan Wang¹, Shasha Liu¹, Xiaoqian Bian¹, Xinyue Chen¹, Liangcun Guo¹, Guoliang Huang¹, Ruimeng Zhao¹, Can Peng¹, Wenhong Jiang¹, Qinglei Shi², Xu Yan², Guang Yang³, and Lina Zhang¹

¹Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China, ²MR Scientific Marketing, Siemens Healthineers Ltd., Beijing, China, ³Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China

Synopsis

In this study, we compared first-order features of six DWI models in predicting the HER-2 expression in breast invasive ductal carcinoma, including conventional mono-exponential model derived parameter ADC, IVIM derived parameters (D, Dstar, f), DKI derived parameters (D, K), SEM derived parameters (DDC, α), FROC derived parameters (D, β, mu), CTRW derived parameters (D, α, β), and a comprehensive diagnostic logistic regression model was established to improve the diagnostic performance. We found that CTRM and IVIM derived first-order features demonstrated the powerful performance, and their combination reached the highest performance. The finding has a great value in breast cancer treatment.

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