Zhong Xi, Tang Hongsheng, Guan Tianpei, Lu Bingui, Zhang Chuangjia, Tang Danlei, Li Jiansheng, Cui Shuzhong
Department of Medical Imaging, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, Guangdong, China.
Department of Abdominal Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, Guangdong, China.
J Clin Transl Hepatol. 2022 Feb 28;10(1):34-41. doi: 10.14218/JCTH.2021.00053. Epub 2021 May 28.
Correct identification of small hepatocellular carcinomas (HCCs) and benign nodules in cirrhosis remains challenging, quantitative apparent diffusion coefficients (ADCs) have shown potential value in characterization of benign and malignant liver lesions. We aimed to explore the added value of ADCs in the identification of small (≤3 cm) HCCs and benign nodules categorized as Liver Imaging Reporting and Data System (LI-RADS) 3 (LR-3) and 4 (LR-4) in cirrhosis.
Ninety-seven cirrhosis patients with 109 small nodules (70 HCCs, 39 benign nodules) of LR-3 and 4 LR-4 based on major and ancillary magnetic resonance imaging features were included. Multiparametric quantitative ADCs of the lesions, including the mean ADC (ADC), minimum ADC (ADC), maximal ADC (ADC), ADC standard deviation (ADC), and mean ADC value ratio of lesion-to-liver parenchyma (ADC) were calculated. Regarding the joint diagnosis, a nomogram model was plotted using multivariate logistic regression analysis. The performance was assessed using the area under the receiver operating characteristic curve (AUC).
The ADC, ADC, ADC, and ADC were significantly associated with the identification of small HCC and benign nodules (<0.001). For the joint diagnosis, the LI-RADS category (odds ratio [OR]=12.50), ADC (OR=0.14), and ADC (OR=0.12) were identified as independent factors for distinguishing HCCs from benign nodules. The joint nomogram model showed good calibration and discrimination, with a C-index of 0.947. Compared with the LI-RADS category alone, this nomogram model demonstrated a significant improvement in diagnostic performance, with AUC increasing from 0.820 to 0.967 (=0.001).
The addition of quantitative ADCs could improve the identification of small HCC and benign nodules categorized as LR-3 and 4 LR-4 in patients with cirrhosis.
准确鉴别肝硬化中的小肝细胞癌（HCC）和良性结节仍具有挑战性，定量表观扩散系数（ADC）在肝脏良恶性病变的特征描述中已显示出潜在价值。我们旨在探讨ADC在鉴别肝硬化中直径≤3 cm的小HCC以及分类为肝脏影像报告和数据系统（LI-RADS）3类（LR-3）和4类（LR-4）的良性结节方面的附加价值。
纳入97例肝硬化患者，这些患者有基于主要和辅助磁共振成像特征的109个LR-3和LR-4的小结节（70个HCC，39个良性结节）。计算病变的多参数定量ADC，包括平均ADC（ADC）、最小ADC（ADC）、最大ADC（ADC）、ADC标准差（ADC）以及病变与肝实质的平均ADC值比（ADC）。关于联合诊断，使用多变量逻辑回归分析绘制列线图模型。使用受试者操作特征曲线下面积（AUC）评估其性能。
ADC、ADC、ADC和ADC与小HCC和良性结节的鉴别显著相关（<0.001）。对于联合诊断，LI-RADS分类（比值比[OR]=12.50）、ADC（OR=0.14）和ADC（OR=0.12）被确定为区分HCC与良性结节的独立因素。联合列线图模型显示出良好的校准和区分能力，C指数为0.947。与单独的LI-RADS分类相比，该列线图模型在诊断性能上有显著改善，AUC从0.820增加到0.967（=0.001）。
添加定量ADC可改善肝硬化患者中分类为LR-3和LR-4的小HCC和良性结节的鉴别。
