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Abstract #4251

Texture analysis of R2* maps for evaluating the pathological grade of hepatocellular carcinoma

Qihao Xu1, Ying Zhao1, Dahua Cui1, Qingwei Song1, Xue Ren1, Tao Lin1, Xin Li2, Yan Guo2, Tingfan Wu2, and Ailian Liu1,3
1Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, Liaoning, China, 2GE Healthcare, Shanghai, China, 3Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian, Liaoning, China


Hepatocellular carcinoma (HCC) is a common malignant tumor with a high mortality. The higher the pathological grade represents lower the differentiation degree of HCC. Patients with low differentiation have a higher postoperative recurrence rate and the worse prognosis. Texture analysis is a post-processing method that highlights the difference between the brightness of pixel features and the intensity of background signals by analyzing the distribution and spatial relationship of gray values, and quantify and evaluate the heterogeneity of tumors at the pixel level. This research suggested that R2* maps texture analysis held great potential in evaluating the pathological grade of HCC.

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