Abstract #1443

Multiparametric prediction model for triple negative breast cancer subtypes using MR parameters including ultrafast DCE MRI

Akane Ohashi¹, Masako Kataoka², Mami Iima², Maya Honda², Rie Ota², Yuta Urushibata ³, Nickel Marcel Dominic ⁴, Masakazu Toi⁵, Yuusuke Hirokawa¹, and Yuji Nakamoto²

¹Radiology, National Hospital Organisation, Kyoto Medical Center, Kyoto, Japan, ²Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan, ³Siemens Healthcare K.K., Tokyo, Japan, ⁴Siemens Healthcare GmbH, Erlangen, Germany, ⁵Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan

We tried to construct MRI-based multiparametric model to predict triple negative (TN) subtype among invasive cancers presenting as masses using 165 lesions (including 26 TN subtype). Maximum slope (MS) and time to enhancement (TTE) from ultrafast (UF)-DCE MRI, apparent diffusion coefficient (ADC), signal to noise ratio on T2-WI, rim enhancement on different phases of the DCE MRI were examined with univariate and multivariate logistic regression analysis. The model using MS from UF-DCE MRI and rim enhancement from early phase of DCE MRI demonstrated the AUC of 0.74 in identifying TN subtype, indicating the MRI’s potential to identify TN subtype noninvasively.

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