Evaluating the severity of Microvascular invasion in hepatocellular carcinoma, by probing the combination of enhancement modes and growth patterns through magnetic resonance imaging

Nomograms in differentiating Microvascular invasion status

Authors

  • Yanzhuo Li Minhang Hospital, Fudan University
  • Sijie Li Department of Radiology, Changhai Hospital, Naval Medical University
  • Yan Lei Department of Radiology, Minhang Hospital, Fudan University
  • Lianlian Liu Department of Radiology, Minhang Hospital, Fudan University
  • Bin Song Minhang Hospital, Fudan University

Abstract

背景。微血管病变(MVI),尤其是肝癌的严重程度,与细胞癌(HCC)的分层相关,但仍不确定哪些影像学特征与MVI分层相关。术前准确预测MVI状态有助于临床医生做出最佳治疗决策。

213例经手术精准的HCC患者根据MVI的严重程度分为三组(M0、M1和M2)。比较各组之间的临床和影像学特征。使用单指标和多指标分析来确定另外,构建列线图以通过关键参数估计MVI和M2等级。利用曲线下(AUC)、曲线曲线和决策曲线分析(DCA)评估列线图的准确性、临床价值和功效。

结果。与MVI相关的4个因素相关(P<0.05),包括非隔离生长类型、无/微增强模式、动脉期瘤周强化、肝胆期瘤周低信号。只有MVI活跃患者的肿瘤最大直径与最小直径之比(Max/Min-R)、汇合多结节生长类型以及非洗洗/洗洗增强模式与M2分级显示出的相关性。识别MVI的眉毛工作特征(ROC)曲线轮廓面积为0.885(95%置信区间[CI]:0.833-0.937),预测M2等级的厚度工作特征(ROC)曲线下面积为0.805(95% CI:0.703-0.908)。列线图显示了DCA和回转曲线的高度优化和临床效果。

结论。术前MRI增强模式和肿瘤价值生长模式是MVI严重程度的独立危险因素,对于个体化决策具有重要意义。

Author Biography

Bin Song, Minhang Hospital, Fudan University

Department of Radiology

Corresponding author

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Published

2025-06-12

How to Cite

Li, Y., Li, S., Lei, Y., Liu, L., & Song, B. (2025). Evaluating the severity of Microvascular invasion in hepatocellular carcinoma, by probing the combination of enhancement modes and growth patterns through magnetic resonance imaging: Nomograms in differentiating Microvascular invasion status. Radiology and Oncology, 59(2), 183–192. Retrieved from https://radioloncol.com/index.php/ro/article/view/4507

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Section

Radiology