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Abstract #4125

Whole-Tumor Histogram and Textural Analysis of Model-based T2 Mapping for the Ki-67 Labeling Index of Breast Cancer

Tianwen Xie1, Qiufeng Zhao2, Caixia Fu3, Robert Grimm4, Tobias Kober5, Tom Hilbert5, 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, 4MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany, 5Advanced Clinical Imaging Technology, Siemens Healthcare AG Switzerland, Lausanne, Switzerland

Recently, there has been increased interest in quantitative MR parameters for assessing tumor proliferation. In this study, we proposed the use of whole-tumor histogram texture features using the model-based T2 mapping method GRAPPATINI to differentiate the positive and negative Ki-67 status of breast cancer. Classification performed between Ki-67-positive and Ki-67-negative groups resulted in an area under the ROC curve of 0.808.

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