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• Clinical Article •
The value of DCE-MRI texture multi-parameter combined analysis to predict the expression of P53 in the differential diagnosis of rectal cancer
LIU Yuhui  CHEN Anliang  WU Jingjun  LIU Tieli  DONG Wan  LIU Yunsong  ZHAO Ying  LIU Ailian 

Cite this article as: Liu YH, Chen AL, Wu JJ, et al. The value of DCE-MRI texture multi-parameter combined analysis to predict the expression of P53 in the differential diagnosis of rectal cancer[J]. Chin J Magn Reson Imaging, 2021, 12(8): 33-37, 74. DOI:10.12015/issn.1674-8034.2021.08.007.


[Abstract] Objective To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis in predicting the expression of P53 in patients with rectal cancer. Materials andMethods A retrospective collection of 91 patients with primary rectal cancer (50 cases in the P53 high expression group and 41 cases in the low expression group) who underwent DCE-MRI scan and had the results of P53 immunohistochemical detection. The post-processing images of Ktrans, Kep, Ve are acquired by GenIQ post-processing software and imported into ITK-SNAP software. Two observers use AK software to extract 12 texture feature parameters respectively, applying intra-group correlation coefficient ICC to test the consistency of the data, using independent sample t test or Mann-Whitney U test to compare two sets of texture parameters, using ROC curve to analyze the identification power of texture parameters. The Logistic regression analysis is used to analyze the efficiency of the combined texture feature parameters, and the difference between the single parameter model and the combined model and the single parameter model AUC is compared through the Delong test.Results The texture parameters of the P53 high expression group, Cluster ProminenceKtrans (23.00±50.84)×106, CorrelationKtrans (1.49±1.42)×10-2, InertiaKep (13.52±22.31)×104, are all larger than the low expression group Cluster ProminenceKtrans(4.89±5.92)×106 , CorrelationKtrans (0.16±0.17)×10-2, InertiaKep (50.52±61.27)×103, the differences were statistically significant (P＜0.05), the AUC values are 0.712, 0.838, 0.638 respectively. The combination of P53 texture parameters with differences between the two groups has a higher diagnostic efficiency, with an AUC of 0.914, a sensitivity of 80%, and a specificity of 95%. After Delong test, there is a difference between the two groups of P53. The AUC of the texture parameter combined model is greater than that of the single parameter model, and the difference is statistically significant (P＜0.05).Conclusions Texture feature parameters Cluster Prominence, Correlation, Inertia can predict the expression of rectal cancer P53 effectively, and the combined analysis of texture analysis feature parameters can achieve higher differential diagnosis performance.
[Keywords] rectal cancer；texture analysis；dynamic enhanced；magnetic resonance imaging；P53

LIU Yuhui1, 2   CHEN Anliang2   WU Jingjun2   LIU Tieli1   DONG Wan2   LIU Yunsong2   ZHAO Ying2   LIU Ailian1, 2*  

1 School of Medical Imaging, Dalian Medical University, Dalian 116044 China

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

Liu AL, E-mail: cjr.liuailian@vip.163.com

Conflicts of interest   None.

Received  2021-04-07
Accepted  2021-05-24
DOI: 10.12015/issn.1674-8034.2021.08.007
Cite this article as: Liu YH, Chen AL, Wu JJ, et al. The value of DCE-MRI texture multi-parameter combined analysis to predict the expression of P53 in the differential diagnosis of rectal cancer[J]. Chin J Magn Reson Imaging, 2021, 12(8): 33-37, 74. DOI:10.12015/issn.1674-8034.2021.08.007.

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