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• Clinical Article •
Preliminary study on correlation between gray level co-occurrence matrix texture analysis based on whole tumor volume measurement ADC image and Ki-67 expression in endometrial cancer
TIAN Shifeng  LIU Ailian  LIU Jinghong  WANG Xuedong  HUANG Kan  GUO Yan  LI Xin 

Cite this article as: Tian SF, Liu AL, Liu JH, et al. Preliminary study on correlation between gray level co-occurrence matrix texture analysis based on whole tumor volume measurement ADC image and Ki-67 expression in endometrial cancer. Chin J Magn Reson Imaging, 2019, 10(11): 826-829. DOI:10.12015/issn.1674-8034.2019.11.006.


[Abstract] Objective: To explore the correlation between gray level co-occurrence matrix (GLCM) texture analysis based on whole tumor volume measurement ADC images and the expression of Ki-67 in endometrial carcinoma (EC).Materials and Methods: The imaging data of 37 patients with EC confirmed by operation and pathology were retrospectively analyzed. ADC images were obtained after post-processing. According to the Ki-67 expression index of EC (<50% was low expression, >50% was high expression), patients were divided into Ki- 67 low expression group (n=17) and Ki-67 high expression group (n=20). Using Omni-Kinetics software, ROI was drawn layer by layer along the edge of the ADC image containing the essence of the tumor. After fusion, the texture parameters of GLCM were obtained, including energy, entropy, inertia, correlation and inverse difference. The difference of GLCM texture parameters between the two groups was compared by independent sample t test (normal distribution) or Mann-Whitney rank sum test (skewed distribution). The ROC curve was used to evaluate the differential diagnostic efficacy of the parameters with statistical difference for the low and high expression groups of Ki-67. Pearson correlation analysis was used to evaluate the correlation between the GLCM parameters and the expression index of Ki-67.Results: The energy and inertia of Ki-67 low expression group were higher than Ki-67 high expression group, and the difference of entropy, correlation and inverse difference were lower than Ki-67 high expression group (P<0.05). The AUC of energy, entropy, inertia, correlation and inverse difference predicted Ki-67 high expression were 0.724, 0.865, 0.803, 0.809 and 0.847, respectively. The energy and inertia were negatively correlated with Ki-67 expression index of EC (P<0.05), the entropy, correlation and inverse difference were positively correlated with Ki-67 expression index (P<0.05).Conclusions: GLCM texture analysis based on whole tumor volume measurement ADC images of is helpful for preoperative evaluation Ki-67 expression of EC. It has certain clinical value and entropy is the best parameter.
[Keywords] endometrial neoplasms；magnetic resonance imaging；texture analysis

TIAN Shifeng Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

LIU Ailian * Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

LIU Jinghong Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

WANG Xuedong Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

HUANG Kan Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

GUO Yan GE Healthcare, Shanghai 200000, China

LI Xin GE Healthcare, Shanghai 200000, China

*Correspondence to: Liu AL, E-mail: liuailian@dmu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS  Program for Training Capital Science and Technology Leading Talents No. Z181100006318003
Received  2019-08-11
DOI: 10.12015/issn.1674-8034.2019.11.006
Cite this article as: Tian SF, Liu AL, Liu JH, et al. Preliminary study on correlation between gray level co-occurrence matrix texture analysis based on whole tumor volume measurement ADC image and Ki-67 expression in endometrial cancer. Chin J Magn Reson Imaging, 2019, 10(11): 826-829. DOI:10.12015/issn.1674-8034.2019.11.006.

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