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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