Texture features of MRI in distinguishing hepatic cell carcinoma from focal nodular hyperplasia
HUANG Weiwei1, HUANG Pan1,2, MENG Qinglin1, LIU Mengqi3, CHEN Zhiye1,2
1. Department of Radiology, Hainan Hospital of PLA General Hospital, Sanya 572013, China; 2. the Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China; 3. Department of Radiology, the First Medical Center of PLA General Hospital, Beijing 100853, China
Abstract：Objective To distinguish hepatic cell carcinoma (HCC) from focal nodular hyperplasia (FNH)based on the texture features of multimodal magnetic resonance imaging (MRI). Methods A retrospective analysis was performed on 38 HCC cases and 14 FNH cases diagnosed pathologically in Hainan Hospital of PLA General Hospital from April 2012 to June 2022. Axial T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) were used for texture analysis, and the texture features included angular second moment (ASM), contrast, correlation, inverse difference moment (IDM) and entropy. Kolmogorov-Smirnov test and Mann-Whitney text were used to compare the differences of textural feature paraments between the two groups, and multiple regression analysis was used to select the variables and their values in the overall model fit, then the regression equation was constructed. Receiver operating characteristic curve was used to evaluate the diagnostic efficacy of the regression equation. Results Based on the texture features of DWI images, it was proved that ASM, correlation and IDM of the FNH group were significantly larger than those in the HCC group, while the contrast and entropy of the FNH group were significantly smaller than those in the HCC group. Based on the texture features of T2WI images, it was confirmed that ASM and correlation of the FNH group were significantly larger than those in the HCC group, while the contrast, IDM and entropy of the FNH group were significantly smaller than those in the HCC group The selected variables of the DWI group in the overall model fitting were entropy, contrast and constant, and the regression equations Y=-1.621×Entropy-0.031×Contrast+12.410 was obtained. The selected variables and their values in T2WI group was the same with the T2WI combined DWI (T2WI-DWI) group. The selected variables were entropy and constant, and the regression equation Y=-2.595×Entropy+16.419 was obtained. The largest area under the receiver operating characteristic curve was 0.945 for the DWI group. Conclusions Texture features of multimodal MRI can effectively distinguish HCC from FNH.
Villanueva A. Hepatocellular Carcinoma[J].New Engl J Med, 2019,380(15):1450-1462.
[2]
Chen X,Yang Z,Deng J.Use of 64-slice spiral CT examinations for hepatocellular carcinoma[J].J Buon, 2019,24(4):1435-1440.
[3]
Bröker M E, Klompenhouwer A J, Gaspersz M P, et al.Growth of focal nodular hyperplasia is not a reason for surgical intervention,but patients should be referred to a tertiary referral centre[J].World J Surg,2018,42(5):1506-1513.
[4]
Mathieu D, Vilgrain V, Mahfouz A E, et al.Benign liver tumors[J]. Magn Reson Imaging C,1997,5(2):255-288.
[5]
Chen T,Dai X,Dai J,et al.AFP promotes HCC progression by suppressing the HuR-mediated Fas/FADD apoptotic pathway[J].Cell Death Dis, 2020,11(10):822.
[6]
Balogh J,Victor D,Asham E H,et al.Hepatocellular carcinoma: a review[J].J Hepatocell Carcino,2016,3:41-53.
[7]
Polychronidis G, Murtha-Lemekhova A, Fuchs J, et al. A multidisciplinary approach to the management of fibrolamellar carcinoma: current perspectives and future prospects[J]. Onco Targets Ther, 2022,15:1095-1103.
[8]
Craig J R,Peters R L,Edmondson H A, et al. Fibrolamellar carcinoma of the liver: a tumor of adolescents and young adults with distinctive clinico-pathologic features[J]. Cancer, 1980,46(2):372-379.
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