切换至 "中华医学电子期刊资源库"
高级检索
中华肝脏外科手术学电子杂志

中华肝脏外科手术学电子杂志 ›› 2025, Vol. 14 ›› Issue (06) : 902 -911. doi: 10.3877/cma.j.issn.2095-3232.2025.06.014

临床研究

基于血清学指标和MRI构建肝纤维化分期诊断双模态模型与验证
曹键1, 孟占鳌1, 张可1, 张悦1, 郭亚豪1, 邓锶锶1, 罗涛1, 朱璇1, 覃杰1, 黎超1, 唐天濠2, 陈颖坤3, 向青1,†()   
  1. 1　510630　广州，中山大学附属第三医院放射科
    2　510030　广州，广东省中医院放射科
    3　510630　广州，天河区石牌街社区卫生服务中心
  • 收稿日期:2025-05-23 出版日期:2025-12-10
  • 通信作者: 向青
  • 基金资助:
    国家自然科学基金(82202129); 广东省自然科学基金(2017A030313841); 医院国家自然科学基金培养项目(2021GZRPYM06); 中山大学第三附属医院五个五工程(2023WW605)

Development and validation of a bimodal model for liver fibrosis staging based on serological indicators and MRI

Jian Cao1, Zhanao Meng1, Ke Zhang1, Yue Zhang1, Yahao Guo1, Sisi Deng1, Tao Luo1, Xuan Zhu1, Jie Qin1, Chao Li1, Tianhao Tang2, Yingkun Chen3, Qing Xiang1,†()   

  1. 1 Department of Radiology, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
    2 Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510030, China
    3 Shipai Community Health Service Center, Guangzhou 510630, China
  • Received:2025-05-23 Published:2025-12-10
  • Corresponding author: Qing Xiang
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

曹键, 孟占鳌, 张可, 张悦, 郭亚豪, 邓锶锶, 罗涛, 朱璇, 覃杰, 黎超, 唐天濠, 陈颖坤, 向青. 基于血清学指标和MRI构建肝纤维化分期诊断双模态模型与验证[J/OL]. 中华肝脏外科手术学电子杂志, 2025, 14(06): 902-911.

Jian Cao, Zhanao Meng, Ke Zhang, Yue Zhang, Yahao Guo, Sisi Deng, Tao Luo, Xuan Zhu, Jie Qin, Chao Li, Tianhao Tang, Yingkun Chen, Qing Xiang. Development and validation of a bimodal model for liver fibrosis staging based on serological indicators and MRI[J/OL]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2025, 14(06): 902-911.

目的

基于血清学指标和MRI构建肝纤维化分期诊断的双模态模型，并探讨其诊断效能。

方法

回顾性分析2022年3月至2023年6月中山大学附属第三医院174例接受多期腹部MRI增强检查患者临床病理学资料。本研究经伦理审查委员会批准，所有患者均签署知情同意书。104例患者为回顾性纳入作为训练集，70例为前瞻性收集作为测试集。其中男120例，女54例；中位年龄53岁。收集MRI多期相快速三维（Quick-3D）序列、肝胆期相对增强率（RLE-HBP）、磁共振弹性成像（MRE）指标，以及AST与血小板比率指数（APRI）、纤维化-4(FIB-4)、Forns指数等血清学指标，肝活检病理学检查作为肝纤维化分期诊断金标准。采用Spearman相关分析评估独立变量和Metavir纤维化分期之间的关系。评价指标组内一致性检验采用类内相关系数（ICC)。根据赤池信息量准则（AIC），采用Logistic回归构建肝纤维化分期诊断双模态模型(RLE-HBP+FIB-4，RLE-HBP+Forns)。采用ROC曲线下面积（AUC）和Delong检验分析双模态模型的诊断性能。

结果

相关性分析显示，MRE与肝纤维化程度为强正相关（rs=0.817，P<0.001)。RLE-HBP与肝纤维化程度为中度负相关（rs=-0.493，P<0.001)。一致性评价显示，在训练集和测试集中MRE和RLE-HBP的ICC值仍然很高（0.893，0.909）。AIC建模显示，RLE-HBP是所有纤维化阶段关键预测因子，FIB-4和Forns指数选择性地提高了模型对≥F1到≥F4阶段肝纤维化诊断的AUC。Delong检验显示，RLE-HBP+FIB-4、RLE-HBP+Forns双模态模型在晚期纤维化诊断（≥F3）获得了与MRE相当的诊断效能（Z=1.273，1.441，P>0.05），优于单模态RLE-HBP模型。双模态模型在测试集中准确性超过70%，验证了它们的临床适用性。

结论

基于血清学指标和MRI成功构建肝纤维化分期诊断双模态模型，RLE-HBP联合FIB-4或Forns指数为晚期肝纤维化诊断提供与MRE相当的诊断准确性，超过了单独的RLE-HBP。

关键词: 肝纤维化, 肝胆期, 纤维化-4, Forns指数, 磁共振弹性成像, 赤池信息量准则
Objective

To construct a bimodal model based on serum indexes and MRI for grading diagnosis of liver fibrosis, and to evaluate their diagnostic efficiency.

Methods

Clinicopathological data of 174 patients receiving multi-phase abdominal contrast-enhanced MRI in the Third Affiliated Hospital of Sun Yat-sen University from March 2022 to June 2023 were retrospectively analyzed. The informed consents of all patients were obtained and the local ethical committee approval was received. 104 patients were retrospectively included in the training set, and 70 cases were prospectively enrolled in the test set. Among them, 120 patients were male and 54 female. The median age was 53 years old. MRI multi-phase quick three-dimensional (Quick-3D) sequence, relative liver enhancement rate in hepatobiliary phase (RLE-HBP), magnetic resonance elastography (MRE) indexes, and other serum indexes such as AST-to-platelet ratio index (APRI), fibrosis-4 (FIB-4) and Forns index were collected. Pathological examination of liver biopsy was considered as the gold standard for grading diagnosis of liver fibrosis. Spearman's correlation analysis was used to evaluate the relationship between independent variables and Metavir fibrosis stage. Intra-class correlation coefficient (ICC) was utilized to test the intra-group consistency of evaluation indexes. According to the Akachi information criterion (AIC), Logistic regression was used to construct the bimodal model for grading diagnosis of liver fibrosis (RLE-HBP+FIB-4, RLE-HBP+Forns). The area under the ROC curve (AUC) and Delong test were adopted to analyze the diagnostic performance of the bimodal model.

Results

Correlation analysis showed a strong positive correlation between MRE and the severity of liver fibrosis (rs=0.817, P<0.001). RLE-HBP had a moderate negative correlation with the severity of liver fibrosis (rs=-0.493, P<0.001). The consistency evaluation indicated that the ICC of MRE and RLE-HBP remained high (0.893, 0.909) in the training and test sets. AIC modeling revealed that RLE-HBP was the key predictor of all stages of fibrosis. FIB-4 and Forns index selectively increased the AUC of the model in the diagnosis from ≥F1 to ≥F4 liver fibrosis. Delong test showed that the bimodal models of RLE-HBP+FIB-4 and RLE-HBP+Forns had equivalent diagnostic efficiency to MRE in the diagnosis of advanced liver fibrosis (≥F3) (Z=1.273, 1.441, both P>0.05), which was superior to the single-mode RLE-HBP model. The accuracy of the bimodal model in the test set exceeded 70%, validating their clinical applicability.

Conclusions

Based on serum indexes and MRI, a bimodal model for grading diagnosis of liver fibrosis are successfully constructed. Compared with MRE, RLE-HBP combined with FIB-4 or Forns index provide equivalent diagnostic accuracy for advanced liver fibrosis, which exceed that of RLE-HBP alone.

Key words: Liver fibrosis, Hepatobiliary phase, Fibrosis-4, Forns index, Magnetic resonance elastography (MRE), Akaike information criterion (AIC)
表1 Metavir肝纤维化分期与患者临床资料相关性
指标 总体(n=174) F0(n= 39) F1(n=29) F2(n=34) F3(n=38) F4(n=34) 统计值 P值
年龄[岁, M(Q1, Q3)] 53 (42, 60) 39 (33, 60) 52 (43, 59) 57 (43, 62) 57 (51, 63) 54 (47, 58) H=12.958 0.011
男性 [例(%)] 120 (69.0) 21 (53.8) 21 (72.4) 22 (64.7) 24 (63.2) 32 (94.1) χ2=15.263 0.004
HBV [例(%)] 81 (46.6) 15 (38.5) 11 (37.9) 15 (44.1) 23 (60.5) 17 (50.0) χ2=5.118 0.275
HCV [例(%)] 12 (6.9) 0 2 (6.9) 3 (8.8) 4 (10.5) 3 (8.8) χ2=5.322 0.287
NASH[例(%)] 57 (32.8) 17 (43.6) 12 (41.4) 12 (35.3) 6 (15.8) 10 (29.4) χ2=8.295 0.081
AIH[例(%)] 24 (13.8) 7 (17.9) 4 (13.8) 4 (11.8) 5 (13.2) 4 (11.8) χ2=0.815 0.951
BMI[kg/m2, M(Q1, Q3)] 22.7 (20.3, 24.8) 21.5 (18.4, 25.3) 22.9 (21.3, 25.0) 23.6 (21.8, 24.8) 21.9 (20.1, 24.2) 23.5 (20.2, 24.8) H=5.433 0.246
Hct[M(Q1, Q3)] 0.39 (0.34, 0.43) 0.39 (0.36, 0.45) 0.42 (0.38, 0.44) 0.40 (0.34, 0.42) 0.39 (0.34, 0.43) 0.35 (0.26, 0.38) H=21.678 <0.001
APRI[M(Q1, Q3)] 4.43 (2.92, 9.56) 3.36 (2.34, 8.44) 3.45 (2.56, 6.16) 4.09 (2.76, 6.47) 5.09 (3.09, 11.87) 8.85 (4.31, 47.29) H=21.401 <0.001
FIB-4[M(Q1, Q3)] 1.67 (1.00, 3.05) 1.00 (0.71, 1.67) 1.12 (0.99, 1.79) 1.87 (0.90, 2.72) 2.47 (1.32, 3.48) 3.30 (1.55, 5.32) H=35.687 <0.001
Forns[M(Q1, Q3)] 8.25 (6.72, 9.73) 7.08 (5.87, 8.52) 7.15 (6.33, 8.39) 8.15 (7.12, 9.52) 8.89 (8.04, 9.74) 9.84(7.92, 11.29) H=27.162 <0.001
表2 Metavir纤维化分期与评估指标组间差异、相关系数和组内一致性评价（
±s）
Metavir分期 MRE RLE-HBP APRI FIB-4 Forns指数
F0 2.3±0.4 1.1±0.4 (0.7±0.7)×10-4 1.4±1.0 7.3±2.0
F1~F4 4.4±1.9 0.7±0.4 (1.2±1.6)×10-4 2.9±2.6 8.7±1.9
t值 -4.864 4.023 -1.350 -2.550 -2.792
P值 <0.001 <0.001 0.180 0.012 0.006
F0~F1 2.5±0.4 1.1±0.4 (0.6±0.7)×10-4 1.5±1.3 7.5±1.8
F2~F4 4.8±2.0 0.6±0.4 (1.3±1.7)×10-4 3.2±2.2 8.9±1.9
t值 -6.932 5.124 -2.316 -3.379 -3.731
P值 <0.001 <0.001 <0.001 0.002 0.003
F0~F2 2.8±0.6 1.0±0.4 (0.7±0.9)×10-4 1.6±1.2 7.6±1.8
F3~F4 5.4±2.0 0.5±0.3 (1.5±1.7)× 10-4 3.7±3.0 9.3±1.8
t值 -9.050 6.542 -3.148 -4.869 -4.655
P值 <0.001 <0.001 0.020 <0.001 <0.001
F0~F3 3.3±1.1 0.9±0.4 (0.8±1.0)×10-4 2.1±2.1 8.0±1.9
F4 6.5±2.2 0.5±0.3 (2.1±2.1)×10-4 4.3±2.9 9.6±1.9
t值 -9.899 4.284 -4.061 -4.207 -3.722
P值 <0.001 <0.001 <0.001 <0.001 <0.001
ICC 0.909 0.893 / / /
rs 0.817 -0.493 0.324 0.450 0.390
P值 <0.001 <0.001 <0.001 <0.001 <0.001
表3 基于AIC准则构建肝纤维化预测模型
Metavir分期 因素 系数 标准误 Z值 P值 95%CI
≥F1 RLE-HBP -1.144 9 0.338 -3.384 0.001 -1.808~-0.482
年龄 0.570 5 0.311 1.835 0.067 -0.039~1.180
FIB-4 1.099 5 0.727 1.513 0.130 -0.325~2.524
≥F2 RLE-HBP -1.150 1 0.291 -3.952 <0.001 -1.720~-0.580
Forns 0.977 5 0.295 3.317 0.001 0.400~1.555
≥F3 RLE-HBP -1.802 3 0.436 -4.132 <0.001 -2.657~-0.947
Forns 0.898 1 0.463 1.942 0.052 -0.008~1.805
性别 -0.807 5 0.341 -2.367 0.018 -1.476~-0.139
FIB-4 1.014 5 0.600 1.691 0.091 -0.162~2.191
FF -0.655 7 0.438 -1.497 0.134 -1.514~0.203
≥F4 RLE-HBP -1.066 8 0.372 -2.866 0.004 -1.796~-0.337
性别 -1.499 1 0.554 -2.705 0.007 -2.585~-0.413
FIB-4 0.889 2 0.328 2.714 0.007 0.247~1.531
表4 训练集中多个指标的ROC曲线分析
Metavir分期 模型 临界值 敏感度 特异度 AUC
≥F1 MRE > 2.782 0 0.881 0.950 0.949
RLE-HBP < 0.796 8 0.619 0.900 0.778
RLE-HBP+APRI > 0.851 3 0.607 0.900 0.781
RLE-HBP+FIB-4 > 0.792 8 0.761 0.850 0.834
RLE-HBP+Forns > 0.849 4 0.678 1.000 0.846
≥F2 MRE > 3.002 0 0.838 0.916 0.936
RLE-HBP < 0.727 6 0.617 0.861 0.776
RLE-HBP+APRI > 0.777 8 0.500 0.972 0.772
RLE-HBP+FIB-4 > 0.643 2 0.691 0.861 0.824
RLE-HBP+Forns > 0.630 1 0.808 0.833 0.841
≥F3 MRE > 4.050 0 0.791 0.982 0.919
RLE-HBP < 0.600 1 0.625 0.928 0.828
RLE-HBP+APRI > 0.640 9 0.666 0.946 0.840
RLE-HBP+FIB-4 > 0.407 1 0.812 0.857 0.883
RLE-HBP+Forns > 0.420 4 0.833 0.857 0.892
≥F4 MRE > 4.888 0 0.791 0.925 0.922
RLE-HBP < 0.527 7 0.625 0.837 0.762
RLE-HBP+APRI > 0.187 3 0.875 0.737 0.812
RLE-HBP+FIB-4 > 0.254 5 0.791 0.812 0.825
RLE-HBP+Forns > 0.186 4 0.833 0.700 0.827
表5 训练集不同模型AUC的比较
Metavir分期 模型 AUC Z值 P值
≥F1 MRE比RLE-HBP 0.949比0.771 3.447 0.000 6
MRE 比 RLE-HBP+FIB-4 0.949比0.808 2.870 0.041 0
MRE 比 RLE-HBP+Forns 0.949比0.805 3.161 0.016 0
RLE-HBP比 RLE-HBP+Forns 0.771比 0.805 0.987 0.323 6
RLE-HBP比 RLE-HBP+FIB-4 0.771比 0.808 1.030 0.303 0
≥F2 MRE比RLE-HBP 0.937比0.788 3.237 0.001 2
MRE 比 RLE-HBP+FIB-4 0.937比0.819 2.861 0.004 2
MRE 比 RLE-HBP+Forns 0.937比0.832 2.655 0.007 9
RLE-HBP比 RLE-HBP+Forns 0.788比0.832 1.458 0.144 8
RLE-HBP比 RLE-HBP+FIB-4 0.788比0.819 0.981 0.326 4
≥F3 MRE比RLE-HBP 0.919比0.824 2.274 0.023 0
MRE 比 RLE-HBP+FIB-4 0.919比0.875 1.273 0.202 9
MRE 比 RLE-HBP+Forns 0.919比0.867 1.441 0.149 6
RLE-HBP比 RLE-HBP+Forns 0.824比 0.867 1.698 0.089 5
RLE-HBP比 RLE-HBP+FIB-4 0.828比 0.875 1.912 0.055 9
≥F4 MRE比RLE-HBP 0.922比0.780 2.626 0.008 6
MRE 比 RLE-HBP+FIB-4 0.922比0.845 1.479 0.139 1
MRE 比 RLE-HBP+Forns 0.922比0.836 1.705 0.088 2
RLE-HBP比 RLE-HBP+Forns 0.780比0.836 1.837 0.066 2
RLE-HBP比 RLE-HBP+FIB-4 0.780比0.845 2.484 0.012 8
表6 测试集中验证双模态模型的准确性
Metavir分期 双模态模型 截止值 ACC 敏感度 特异度
≥F1 RLE-HBP+FIB-4 0.62 0.77 0.63 0.82
RLE-HBP+Forns 0.64 0.84 0.58 0.94
≥F2 RLE-HBP+FIB-4 0.43 0.79 0.47 0.95
RLE-HBP+Forns 0.45 0.73 0.56 0.87
≥F3 RLE-HBP+FIB-4 0.26 0.73 0.63 0.92
RLE-HBP+Forns 0.29 0.70 0.63 0.83
≥F4 RLE-HBP+FIB-4 0.11 0.70 0.67 0.90
RLE-HBP+Forns 0.14 0.76 0.75 0.80
图1 肝脏Metavir纤维化分期图像MRE勾画和病理学检查图像 注：a~d为例1患者，女，42岁，分别为T1-Unenh，T1-HBP，MRE和病理学检查图像（HE，×400）；RLE-HBP为0.79，MRE为3.82 kPa，确定患者肝纤维分期<F3；e~h为例2患者，女，37岁，分别为T1-Unenh、T1-HBP、MRE和病理学检查图像（HE，×400），RLE-HBP为0.54，MRE为4.85 kPa，成功确定肝纤维化分期≥F3，与病理诊断一致（F3分期）；T1-Unenh为非对比增强的T1信号强度，T1-HBP为增强肝胆期肝脏T1信号强度，MRE为磁共振弹性成像，RLE-HBP为肝胆期相对增强率
[1]
Karsdal MA, Manon-Jensen T, Genovese F, et al. Novel insights into the function and dynamics of extracellular matrix in liver fibrosis[J]. Am J Physiol Gastrointest Liver Physiol, 2015, 308(10): G807-G830. DOI: 10.1152/ajpgi.00447.2014.
[2]
Lu Y, Liu J, Lin C, et al. Peroxiredoxin 2: a potential biomarker for early diagnosis of hepatitis B virus related liver fibrosis identified by proteomic analysis of the plasma[J]. BMC Gastroenterol, 2010, 10: 115. DOI: 10.1186/1471-230X-10-115.
[3]
Naoumov NV, Brees D, Loeffler J, et al. Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH[J]. J Hepatol, 2022, 77(5): 1399-1409. DOI: 10.1016/j.jhep.2022.06.018.
[4]
Sterling RK, Vilar-Gomez E, Wilson LA, et al. Diagnostic ability of simple noninvasive blood tests to predict increased liver stiffness in people living with HIV and steatotic liver disease[J]. Am J Gastroenterol, 2024, 119(8): 1483-1495. DOI: 10.14309/ajg.0000000000002700.
[5]
Zakareya T, Elhelbawy M, Elzohry H, et al. Long-term impact of hepatitis C virus eradication on liver stiffness in Egyptian patients[J]. Can J Gastroenterol Hepatol, 2021, 2021: 4961919. DOI: 10.1155/2021/4961919.
[6]
Khalifa A, Rockey DC. The utility of liver biopsy in 2020[J]. Curr Opin Gastroenterol, 2020, 36(3): 184-191. DOI: 10.1097/MOG.0000000000000621.
[7]
Virarkar M, Morani AC, Taggart MW, et al. Liver fibrosis assessment[J]. Semin Ultrasound CT MR, 2021, 42(4): 381-389. DOI: 10.1053/j.sult.2021.03.003.
[8]
Martínez SM, Crespo G, Navasa M, et al. Noninvasive assessment of liver fibrosis[J]. Hepatology, 2011, 53(1): 325-335. DOI: 10.1002/hep.24013.
[9]
Manduca A, Bayly PJ, Ehman RL, et al. MR elastography: principles, guidelines, and terminology[J]. Magn Reson Med, 2021, 85(5): 2377-2390. DOI: 10.1002/mrm.28627.
[10]
Mallet V, Parlati L, Vallet-Pichard A, et al. FIB-4 index to rule-out advanced liver fibrosis in NAFLD patients[J]. Presse Med, 2019, 48(12): 1484-1488. DOI: 10.1016/j.lpm.2019.10.017.
[11]
Ballestri S, Mantovani A, Baldelli E, et al. Liver fibrosis biomarkers accurately exclude advanced fibrosis and are associated with higher cardiovascular risk scores in patients with NAFLD or viral chronic liver disease[J]. Diagnostics, 2021, 11(1): 98. DOI: 10.3390/diagnostics11010098.
[12]
Bastati N, Perkonigg M, Sobotka D, et al. Correlation of histologic, imaging, and artificial intelligence features in NAFLD patients, derived from Gd-EOB-DTPA-enhanced MRI: a proof-of-concept study[J]. Eur Radiol, 2023, 33(11): 7729-7743. DOI: 10.1007/s00330-023-09735-5.
[13]
Dominguez A, Fino D, Spina JC, et al. Assessment of SE-MRE-derived shear stiffness at 3. 0 Tesla for solid liver tumors characterization[J]. Abdom Radiol, 2021, 46(5): 1904-1911. DOI: 10.1007/s00261-020-02828-5.
[14]
Dzyubak B, Glaser K, Yin M, et al. Automated liver stiffness measurements with magnetic resonance elastography[J]. J Magn Reson Imaging, 2013, 38(2): 371-379. DOI: 10.1002/jmri.23980.
[15]
Kumada T, Toyoda H, Yasuda S, et al. Liver stiffness measurements by 2D shear-wave elastography: effect of steatosis on fibrosis evaluation[J]. AJR Am J Roentgenol, 2022, 219(4): 604-612. DOI: 10.2214/AJR.22.27656.
[16]
Beer L, Mandorfer M, Bastati N, et al. Inter-and intra-reader agreement for gadoxetic acid-enhanced MRI parameter readings in patients with chronic liver diseases[J]. Eur Radiol, 2019, 29(12): 6600-6610. DOI: 10.1007/s00330-019-06182-z.
[17]
Chowdhury AB, Mehta KJ. Liver biopsy for assessment of chronic liver diseases: a synopsis[J]. Clin Exp Med, 2023, 23(2): 273-285. DOI: 10.1007/s10238-022-00799-z.
[18]
Roh YH, Kang BK, Jun DW, et al. Role of FIB-4 for reassessment of hepatic fibrosis burden in referral center[J]. Sci Rep, 2021, 11(1): 13616. DOI: 10.1038/s41598-021-93038-6.
[19]
Dobseu R, Nanfack A, Kowo M, et al. Evaluation of hepatic fibrosis in HIV/HCV co-infected individuals in Yaoundé, Cameroon: usefulness of APRI score in resource-constrained settings[J]. BMC Infect Dis, 2020, 20(1): 758. DOI: 10.1186/s12879-020-05477-7.
[20]
Izumi G, Takeda A, Yamazawa H, et al. Forns Index is a predictor of cardiopulmonary bypass time and outcomes in Fontan conversion[J]. Heart Vessels, 2020, 35(4): 586-592. DOI: 10.1007/s00380-019-01515-w.
[21]
Torres L, Schuch A, Longo L, et al. New FIB-4 and NFS cutoffs to guide sequential non-invasive assessment of liver fibrosis by magnetic resonance elastography in NAFLD[J]. Ann Hepatol, 2023, 28(1): 100774. DOI: 10.1016/j.aohep.2022.100774.
[22]
Zeng Q, Honarvar M, Schneider C, et al. Three-dimensional multi-frequency shear wave absolute vibro-elastography (3D S-WAVE) with a matrix array transducer: implementation and preliminary in vivo study of the liver[J]. IEEE Trans Med Imag, 2021, 40(2): 648-660. DOI: 10.1109/TMI.2020.3034065.
[23]
Kromrey ML, Le Bihan D, Ichikawa S, et al. Diffusion-weighted MRI-based virtual elastography for the assessment of liver fibrosis[J]. Radiology, 2020, 295(1): 127-135. DOI: 10.1148/radiol.2020191498.
[24]
Jang W, Jo S, Song JS, et al. Comparison of diffusion-weighted imaging and MR elastography in staging liver fibrosis: a meta-analysis[J]. Abdom Radiol, 2021, 46(8): 3889-3907. DOI: 10.1007/s00261-021-03055-2.
[25]
Le Bihan D, Ichikawa S, Motosugi U. Diffusion and intravoxel incoherent motion MR imaging-based virtual elastography: a hypothesis-generating study in the liver[J]. Radiology, 2017, 285(2): 609-619. DOI: 10.1148/radiol.2017170025.
[26]
Brenig R, Bernsmeier C. Liver cirrhosis[J]. Praxis (Bern 1994), 2023, 112(11): 554-561.
[27]
Thomeer MG, Vanhooymissen IJSML, Braun LMM, et al. Response to letter: intrapatient comparison of the hepatobiliary phase of Gd-BOPTA and Gd-EOB-DTPA in the differentiation of HCA from FNH[J]. J Magn Reson Imaging, 2020, 52(4): 1281-1282. DOI: 10.1002/jmri.27137.
[28]
Dahlqvist Leinhard O, Dahlström N, Kihlberg J, et al. Quantifying differences in hepatic uptake of the liver specific contrast agents Gd-EOB-DTPA and Gd-BOPTA: a pilot study[J]. Eur Radiol, 2012, 22(3): 642-653. DOI: 10.1007/s00330-011-2302-4.
[29]
Feier D, Balassy C, Bastati N, et al. The diagnostic efficacy of quantitative liver MR imaging with diffusion-weighted, SWI, and hepato-specific contrast-enhanced sequences in staging liver fibrosis: a multiparametric approach[J]. Eur Radiol, 2016, 26(2): 539-546. DOI: 10.1007/s00330-015-3830-0.
[30]
Zhao X, Huang M, Zhu Q, et al. The relationship between liver function and liver parenchymal contrast enhancement on Gd-BOPTA-enhanced MR imaging in the hepatocyte phase[J]. Magn Reson Imaging, 2015, 33(6): 768-773. DOI: 10.1016/j.mri.2015.03.006.
[31]
Khouri Chalouhi C, Vernuccio F, Rini F, et al. Hepatobiliary phase in cirrhotic patients with different Model for End-stage Liver Disease score: comparison of the performance of gadoxetic acid to gadobenate dimeglumine[J]. Eur Radiol, 2019, 29(6): 3090-3099. DOI: 10.1007/s00330-018-5884-2.
[32]
Friedman SL, Pinzani M. Hepatic fibrosis 2022: unmet needs and a blueprint for the future[J]. Hepatology, 2022, 75(2): 473-488. DOI: 10.1002/hep.32285.
[33]
Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review[J]. Brief Bioinform, 2022, 23(2): bbab569. DOI: 10.1093/bib/bbab569.
[34]
Kaffe E, Roulis M, Zhao J, et al. Humanized mouse liver reveals endothelial control of essential hepatic metabolic functions[J]. Cell, 2023, 186(18): 3793-3809. e26. DOI: 10.1016/j.cell.2023.07.017.
[35]
Reiberger T. The value of liver and spleen stiffness for evaluation of portal hypertension in compensated cirrhosis[J]. Hepatol Commun, 2022, 6(5): 950-964. DOI: 10.1002/hep4.1855.
[1] 高建松, 陈晓晓, 冯婷, 包剑锋, 魏淑芳, 潘林. 基于超声瞬时弹性成像的多参数决策树模型评估慢性乙型肝炎患者肝纤维化等级[J/OL]. 中华医学超声杂志（电子版）, 2023, 20(09): 923-929.
[2] 骆云凯, 鄢曹鑫, 张宣宣, 李如梅, 王文倩, 洪行行, 夏斌, 邹伟璞, 张珊珊, 陈剑. 声触诊弹性成像检测脾硬度对诊断慢性乙肝肝纤维化程度的应用价值[J/OL]. 中华医学超声杂志（电子版）, 2022, 19(11): 1232-1237.
[3] 李沁园, 董常峰, 冯程, 李志艳, 刘李, 何秉昊, 姜伟, 田文硕, 杨帅. 基于弹性成像多模态法评估慢性乙型肝炎肝纤维化程度[J/OL]. 中华医学超声杂志（电子版）, 2022, 19(09): 976-982.
[4] 施娜, 邱文倩, 何丹青, 张超学, 毛萍, 杨艳婷. 瞬时弹性成像评价慢性乙型肝炎患者抗病毒治疗的效果[J/OL]. 中华医学超声杂志（电子版）, 2022, 19(05): 428-433.
[5] 李敏, 杨世英, 高晓琴, 周丹, 唐筱, 张立婷. 维生素A与慢性肝病相关性研究进展[J/OL]. 中华实验和临床感染病杂志(电子版), 2024, 18(02): 65-70.
[6] 张麒, 马臻. 丙型肝炎肝纤维化进展影响因素及标志物研究进展[J/OL]. 中华实验和临床感染病杂志(电子版), 2024, 18(01): 1-6.
[7] 李雪铭, 伊诺, 卢智豪, 冯婧, 董健藤, 李健. 人脐带间充质干细胞来源外泌体抑制肝星状细胞活化发挥抗肝纤维化作用的实验研究[J/OL]. 中华细胞与干细胞杂志(电子版), 2025, 15(03): 148-156.
[8] 刀辰冉, 陈义发, 阿力木江·买合木提, 徐磊, 陈姚. S指数对符合米兰标准的肝癌合并慢性乙肝患者术后预后的预测价值[J/OL]. 中华肝脏外科手术学电子杂志, 2021, 10(03): 274-278.
[9] 李娅敏, 吕新蕾, 史秀梅, 王月云, 盛欧, 卜高峰, 成松, 谢士宁. 富马酸丙酚替诺福韦治疗慢性乙型肝炎获得病毒学应答后肝纤维化逆转的影响因素[J/OL]. 中华消化病与影像杂志(电子版), 2025, 15(04): 359-363.
[10] 江浩, 余宏圣, 杨碧兰, 阿布都克尤木·斯马依, 吴斌, 杨逸冬. 基于列线图模型对慢性乙型肝炎合并肝脏脂肪变性患者并发晚期肝纤维化的临床预测[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(02): 114-120.
[11] 丁茜, 李振, 王思宁, 赵连晖, 王广川, 张春清. 人肝窦内皮细胞中α1肾上腺素能受体亚型表达的初步实验研究[J/OL]. 中华消化病与影像杂志(电子版), 2022, 12(02): 98-101.
[12] 李明凯, 吴晓瑛, 吴斌. 磁共振弹性成像技术在慢性肝病的临床应用研究进展[J/OL]. 中华消化病与影像杂志(电子版), 2021, 11(03): 126-131.
[13] 李浩, 陈棋帅, 费发珠, 张宁伟, 李元东, 王硕晨, 任宾. 慢性肝病肝纤维化无创诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(09): 863-867.
[14] 刘志强, 窦项洁, 刘白露, 董晓萌, 鲍俊宇. 银杏达莫注射液对大鼠肝缺血再灌注损伤的保护作用机制研究[J/OL]. 中华诊断学电子杂志, 2022, 10(04): 259-265.
[15] 张娜, 鲁志宏, 张盛军, 陈启众, 俞永涛, 陈申思, 钱海权, 刘轲. 腹腔镜袖状胃切除术治疗肥胖合并非酒精性脂肪肝患者肝纤维化的短期疗效分析[J/OL]. 中华肥胖与代谢病电子杂志, 2023, 09(04): 261-266.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号

AI
小
编
AI小编
你好！我是《中华医学电子期刊资源库》AI小编，有什么可以帮您的吗？