Abstract #0285

Can Machine-Learning-based Radiomics of Whole Tumor on MR Multiparametric Maps Predict the Ki-67 index of Breast Cancer?

Tianwen Xie¹, Qiufeng Zhao², Caixia Fu³, Robert Grimm⁴, Yajia Gu¹, and Weijun Peng¹

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

There has recently been increased interest in quantitative MR parameters for assessing tumor proliferation. A total of 134 consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively evaluated. We extracted the whole-tumor histogram and textural features from an ADC map and DCE-MRI semi-quantitative maps. The LASSO for feature selection and the KNN algorithm for classification were performed. Classifications performed between Ki-67-positive and Ki-67-negative groups resulted in an accuracy of 75.4% using three texture features, whereas classification with only the entropy of ADC yielded an accuracy of 74.6%.

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