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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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