Abstract #4337

Whole-Tumor Histogram Analysis of Multiparametric MRI for Subtype Classification of Breast Cancer

Tianwen Xie¹, Qiufeng Zhao², Robert Grimm³, Caixia Fu⁴, Yajia Gu¹, and Weijun Peng¹

¹Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China, ²Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China, ³MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany, ⁴Application Development, Siemens Shenzhen Magnetic Resonance, Shenzhen, China

Recently, several studies have shown the value of Magnetic Resonance Imaging (MRI) radiomics in non-invasive lesion subtype classification. In this study, we proposed the use of histogram texture features of multiparametric maps to differentiate subtypes of breast cancer. 34 different whole-tumor histogram features were analyzed. Classification was performed between ER-positive and Triple-negative groups resulted in AUROC of 0.94, while classification between ER-positive and HER2-positive groups, and classification between HER2-positive and Triple-negative yielded AUROC of 0.79, and 0.86, respectively.

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