Texture features of MRI in distinguishing hepatic cell carcinoma from focal nodular hyperplasia

HUANG Weiwei; HUANG Pan; MENG Qinglin; LIU Mengqi; CHEN Zhiye

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Medical Journal of the Chinese People Armed Police Forces ›› 2024, Vol. 35 ›› Issue (8) : 685-689.

Texture features of MRI in distinguishing hepatic cell carcinoma from focal nodular hyperplasia

HUANG Weiwei¹, HUANG Pan^1,2, MENG Qinglin¹, LIU Mengqi³, CHEN Zhiye^1,2

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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.

Key words

hepatic cell carcinoma / focal nodular hyperplasia / diffusion weighted imaging / T2 weighted imaging / texture feature

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HUANG Weiwei, HUANG Pan, MENG Qinglin, LIU Mengqi, CHEN Zhiye. Texture features of MRI in distinguishing hepatic cell carcinoma from focal nodular hyperplasia[J]. Medical Journal of the Chinese People Armed Police Forces. 2024, 35(8): 685-689

References

[1] 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.
[9] 胡利红,李京,佟旭,等.CT图像肝纹理分析在肝癌评估中的应用价值观察[J].中国CT和MRI杂志, 2022,20(9):121-123.
[10] 李明通.肝脏局灶性结节性增生和甲胎蛋白阴性肝细胞癌患者的影像学特点分析[J].临床肝胆病杂志, 2015,31(5):729-732.
[11] 李跃明,邢振,林娜,等.3.0TMRI在小肝细胞癌与肝脏局灶性结节增生中的鉴别诊断价值[J].中国医学影像学杂志, 2014,22(7):501-504.
[12] James A. Focal lesions of the liver: imaging appearances and management[J].Brit J Hosp Med,2020,81(11):1-22.
[13] 周晓霞,张景,刘卓,等.基于MR图像女性阿尔茨海默症海马纹理特征研究[J].北京生物医学工程, 2010,29(3):230-234.
[14] Mayerhoefer M E, Materka A, Langs G, et al.Introduction to radiomics[J].J Nucl Med, 2020,61(4):488-495.
[15] 王禹博,王顺涛,谢磊,等.基于扩散加权成像的影像组学模型鉴别诊断肝细胞癌与肝血管瘤[J].临床放射学杂志, 2020,39(3):481-486.
[16] 王慧慧,赵心明,郭炜,等.增强CT纹理特征在鉴别肝脏局灶性结节性增生与肝细胞肝癌中的应用价值[J].癌症进展, 2017,15(5):576-579,583.
[17] 林莹,李大创,李鹏.磁共振平扫T₂加权成像序列图像的纹理特征在肝囊肿与肝血管瘤鉴别诊断中的价值[J].中国临床医师杂志, 2022,50(6):690-693.
[18] 黄小童,陈黎,田菁,等.基于灰度共生矩阵信息融合的视频噪声识别算法[J].计算机应用与软件, 2015,32(2):143-147.
[19] 王新亭,陈欣菊.肝细胞癌起源的研究[J].现代肿瘤医学, 2017,25(6):978-981.
[20] 沈新平,冯飞,聂伟霞,等.DWI鉴别诊断实质性肝脏肿瘤的价值[J].中国医学影像技术,2011,27(11):2250-2252.
[21] 郑晓东,唐亮.CT和MRI增强扫描对肝脏局灶性结节性增生的诊断价值[J].肝脏,2019,24(12):1468-1470.
[22] 王波涛,何蕾,刘刚,等.磁共振成像纹理特征分析在胰腺浆液性囊腺瘤及黏液性囊腺瘤鉴别诊断中的价值[J].中国医学科学院学报, 2018,40(2):187-193.
[23] 宋花玲,贺佳,虞慧婷,等.应用ROC曲线下面积对两相关诊断试验进行评价和比较[J].第二军医大学学报, 2006(5):562-563.