Preoperative and postoperative MRI-based models versus clinical staging systems for predicting early recurrence in hepatocellular carcinoma

被引:0
|
作者
Lu, Ye [1 ]
Wang, Huanhuan [1 ]
Li, Chenxia [2 ]
Faghihkhorasani, Ferdos [3 ]
Guo, Cheng [1 ]
Zheng, Xin [1 ]
Song, Tao [1 ]
Liu, Qingguang [1 ]
Han, Shaoshan [1 ]
机构
[1] Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Hepatobiliary Surg, Xian, Peoples R China
[2] Xi An Jiao Tong Univ, Affiliated Hosp 1, Dept Diagnost Radiol, Xian, Shaanxi, Peoples R China
[3] Xi An Jiao Tong Univ, Med Campus, Xian, Shaanxi, Peoples R China
来源
EJSO | 2024年 / 50卷 / 09期
基金
中国国家自然科学基金;
关键词
Hepatocellular carcinoma; Early recurrence; Predictive model; Gd-EOB-DTPA enhanced MRI; GADOXETIC ACID; HEPATITIS-B; ASPARTATE-AMINOTRANSFERASE; MICROVASCULAR INVASION; PLATELET RATIO; CIRRHOSIS; RESECTION; FIBROSIS; INDEX;
D O I
10.1016/j.ejso.2024.108476
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: To predict the early recurrence of HCC patients who received radical resection using preoperative variables based on Gd-EOB-DTPA enhanced MRI, followed by the comparison with the postoperative model and clinical staging systems. Methods: One hundred and twenty-nine HCC patients who received radical resection were categorized into the early recurrence group (n = 48) and the early recurrence-free group (n = 81). Through COX regression analysis, statistically significant variables of laboratory, pathologic, and Gd-EOB-DTPA enhanced MRI results were identified. The preoperative and postoperative models were established to predict early recurrence, and the prognostic performances and differences were compared between the two models and clinical staging systems. Results: Six variables were incorporated into the preoperative model, including alpha-fetoprotein (AFP) level, aspartate aminotransferase/platelet ratio index (APRI), rim arterial phase hyperenhancement (rim APHE), peritumoral hypointensity on hepatobiliary phase (HBP), CERHBP (tumor-to-liver SI ratio on hepatobiliary phase imaging), and ADC value. Moreover, the postoperative model was developed by adding microvascular invasion (MVI) and histological grade. The C-index of the preoperative model and postoperative model were 0.889 and 0.901 (p = 0.211) respectively. Using receiver operating characteristic curve analysis (ROC) and decision curve analysis (DCA), it was determined that the innovative models we developed had superior predictive capabilities for early recurrence in comparison to current clinical staging systems. HCC patients who received radical resection were stratified into low-, medium-, and high-risk groups on the basis of the preoperative and postoperative models. Conclusion: The preoperative and postoperative MRI-based models built in this study were more competent compared with clinical staging systems to predict the early recurrence in hepatocellular carcinoma.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Comparison of a preoperative MR-based recurrence risk score versus the postoperative score and four clinical staging systems in hepatocellular carcinoma: a retrospective cohort study
    Hong Wei
    Hanyu Jiang
    Yun Qin
    Yuanan Wu
    Jeong Min Lee
    Fang Yuan
    Tianying Zheng
    Ting Duan
    Zhen Zhang
    Yali Qu
    Jie Chen
    Yuntian Chen
    Zheng Ye
    Shan Yao
    Lin Zhang
    Ting Yang
    Bin Song
    European Radiology, 2022, 32 : 7578 - 7589
  • [2] Comparison of a preoperative MR-based recurrence risk score versus the postoperative score and four clinical staging systems in hepatocellular carcinoma: a retrospective cohort study
    Wei, Hong
    Jiang, Hanyu
    Qin, Yun
    Wu, Yuanan
    Lee, Jeong Min
    Yuan, Fang
    Zheng, Tianying
    Duan, Ting
    Zhang, Zhen
    Qu, Yali
    Chen, Jie
    Chen, Yuntian
    Ye, Zheng
    Yao, Shan
    Zhang, Lin
    Yang, Ting
    Song, Bin
    EUROPEAN RADIOLOGY, 2022, 32 (11) : 7578 - 7589
  • [3] Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study
    Hui, T. C. H.
    Chuah, T. K.
    Low, H. M.
    Tan, C. H.
    CLINICAL RADIOLOGY, 2018, 73 (12) : 1056.e11 - 1056.e16
  • [4] Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization
    Xinyu Guo
    Jingjing Song
    Lingyi Zhu
    Shuang Liu
    Chaoming Huang
    Lingling Zhou
    Weiyue Chen
    Guihan Lin
    Zhongwei Zhao
    Jianfei Tu
    Minjiang Chen
    Feng Chen
    Liyun Zheng
    Jiansong Ji
    BMC Cancer, 25 (1)
  • [5] Preoperative and postoperative nomograms for predicting early recurrence of hepatocellular carcinoma without macrovascular invasion after curative resection
    Yanfang Zhang
    Xuezhong Lei
    Liangliang Xu
    Xiaoju Lv
    Mingqing Xu
    Hong Tang
    BMC Surgery, 22
  • [6] Preoperative and postoperative nomograms for predicting early recurrence of hepatocellular carcinoma without macrovascular invasion after curative resection
    Zhang, Yanfang
    Lei, Xuezhong
    Xu, Liangliang
    Lv, Xiaoju
    Xu, Mingqing
    Tang, Hong
    BMC SURGERY, 2022, 22 (01)
  • [7] MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma
    Wang, Qinghua
    Zhou, Yongjie
    Yang, Hongan
    Zhang, Jingrun
    Zeng, Xianjun
    Tan, Yongming
    MEDICAL PHYSICS, 2024, 51 (07) : 4673 - 4686
  • [8] Multiphase MRI-Based Radiomics for Predicting Histological Grade of Hepatocellular Carcinoma
    Yang, Yan
    Zhang, Si
    Cui, Chun
    Pen, Chao-qun
    Mu, Ke
    Zhang, Dong
    Wen, Li
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2024, 60 (05) : 2117 - 2127
  • [9] Preoperative MRI in predicting tumour recurrence of hepatocellular carcinoma: Does AI have a role?
    Huo, Teh-Ia
    Ho, Shu-Yein
    LIVER INTERNATIONAL, 2024, 44 (07) : 1739 - 1739
  • [10] Texture Analysis Based on Preoperative Magnetic Resonance Imaging (MRI) and Conventional MRI Features for Predicting the Early Recurrence of Single Hepatocellular Carcinoma after Hepatectomy
    Zhang, Jing
    Liu, Xinjie
    Zhang, Haiping
    He, Xiaojing
    Liu, Yangyang
    Zhou, Jun
    Guo, Dajing
    ACADEMIC RADIOLOGY, 2019, 26 (09) : 1164 - 1173