Wengert Georg J, Baltzer Pascal A T, Bickel Hubert, Thurner Patrick, Breitenseher Julia, Lazar Mathias, Pones Matthias, Peck-Radosavljevic Markus, Hucke Florian, Ba-Ssalamah Ahmed
Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer-Guertel 18-20, 1090Vienna, Austria.
Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer-Guertel 18-20, 1090Vienna, Austria.
Acad Radiol. 2017 Dec;24(12):1491-1500. doi: 10.1016/j.acra.2017.06.005. Epub 2017 Jul 26.
This study aimed to investigate the potential of contrast-enhanced magnetic resonance imaging features to differentiate between mass-forming intrahepatic cholangiocellular carcinoma (ICC) and hepatocellular carcinoma (HCC) in cirrhotic livers.
This study, performed between 2001 and 2013, included 64 baseline magnetic resonance imaging examinations with pathohistologically proven liver cirrhosis, presenting with either ICC (n = 32) or HCC (n = 32) tumors. To distinguish ICC form HCC tumors, 20 qualitative single-lesion descriptors were evaluated by two readers, in consensus, and statistically classified using the chi-square automatic interaction detection (CHAID) methodology. Diagnostic performance was assessed by a receiver operating characteristic analysis.
The CHAID algorithm identified three independent categorical lesion descriptors, including (1) liver capsular retraction; (2) progressive or persistent enhancement pattern or wash-out on the T1-weighted delayed phase; and (3) signal intensity appearance on T2-weighted images that could help to reliably differentiate ICC from HCC, which resulted in an AUC of 0.807, and a sensitivity and specificity of 68.8 and 90.6 (95% confidence interval 75.0-98.0), respectively.
The proposed CHAID algorithm provides a simple and robust step-by-step classification tool for a reliable and solid differentiation between ICC and HCC tumors in cirrhotic livers.
本研究旨在探讨对比增强磁共振成像特征在鉴别肝硬化肝脏中肿块型肝内胆管癌(ICC)和肝细胞癌(HCC)方面的潜力。
本研究于2001年至2013年进行,纳入64例经病理组织学证实为肝硬化的基线磁共振成像检查,其中存在ICC肿瘤(n = 32)或HCC肿瘤(n = 32)。为区分ICC和HCC肿瘤,两名阅片者共同评估了20个定性单病灶描述符,并使用卡方自动交互检测(CHAID)方法进行统计分类。通过受试者操作特征分析评估诊断性能。
CHAID算法确定了三个独立的分类病灶描述符,包括(1)肝包膜回缩;(2)T1加权延迟期的渐进性或持续性强化模式或廓清;(3)T2加权图像上的信号强度表现,这些有助于可靠地区分ICC和HCC,其曲线下面积(AUC)为0.807,敏感性和特异性分别为68.8%和90.6%(95%置信区间75.0 - 98.0)。
所提出的CHAID算法为可靠且准确地区分肝硬化肝脏中的ICC和HCC肿瘤提供了一种简单且稳健的逐步分类工具。