Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, 100 West Fourth Ring Middle Road, Feng Tai District, Beijing, 100853, China.
Department of Ultrasound, Aero-space Center Hospital, Beijing, China.
Cancer Imaging. 2023 Jun 19;23(1):63. doi: 10.1186/s40644-023-00573-8.
CEUS LI-RADS (Contrast Enhanced Ultrasound Liver Imaging Reporting and Data System) has good diagnostic efficacy for differentiating hepatic carcinoma (HCC) from solid malignant tumors. However, it can be problematic in patients with both chronic hepatitis B and extrahepatic primary malignancy. We explored the diagnostic performance of LI-RADS criteria and CEUS-based machine learning (ML) models in such patients.
Consecutive patients with hepatitis and HCC or liver metastasis (LM) who were included in a multicenter liver cancer database between July 2017 and January 2022 were enrolled in this study. LI-RADS and enhancement features were assessed in a training cohort, and ML models were constructed using gradient boosting, random forest, and generalized linear models. The diagnostic performance of the ML models was compared with LI-RADS in a validation cohort of patients with both chronic hepatitis and extrahepatic malignancy.
The mild washout time was adjusted to 54 s from 60 s, increasing accuracy from 76.8 to 79.4%. Through feature screening, washout type II, rim enhancement and unclear border were identified as the top three predictor variables. Using LI-RADS to differentiate HCC from LM, the sensitivity, specificity, and AUC were 68.2%, 88.6%, and 0.784, respectively. In comparison, the random forest and generalized linear model both showed significantly higher sensitivity and accuracy than LI-RADS (0.83 vs. 0.784; all P < 0.001).
Compared with LI-RADS, the random forest and generalized linear model had higher accuracy for differentiating HCC from LM in patients with chronic hepatitis B and extrahepatic malignancy.
CEUS LI-RADS（对比增强超声肝脏成像报告和数据系统）在鉴别肝细胞癌（HCC）与实体恶性肿瘤方面具有良好的诊断效能。然而，对于同时患有慢性乙型肝炎和肝外原发性恶性肿瘤的患者，其诊断性能可能存在问题。我们探讨了 LI-RADS 标准和基于 CEUS 的机器学习（ML）模型在这类患者中的诊断性能。
本研究纳入了 2017 年 7 月至 2022 年 1 月期间在多中心肝癌数据库中连续入组的患有肝炎和 HCC 或肝转移（LM）的患者。在训练队列中评估 LI-RADS 和增强特征，并使用梯度提升、随机森林和广义线性模型构建 ML 模型。在同时患有慢性肝炎和肝外恶性肿瘤的验证队列中，比较了 ML 模型与 LI-RADS 的诊断性能。
将轻度廓清时间从 60 秒调整为 54 秒，准确率从 76.8%提高到 79.4%。通过特征筛选，廓清类型 II、边缘增强和边界不清被确定为前三个预测变量。使用 LI-RADS 区分 HCC 和 LM，其敏感性、特异性和 AUC 分别为 68.2%、88.6%和 0.784。相比之下，随机森林和广义线性模型的敏感性和准确率均显著高于 LI-RADS（0.83 比 0.784；均 P<0.001）。
与 LI-RADS 相比，随机森林和广义线性模型在同时患有慢性乙型肝炎和肝外恶性肿瘤的患者中，对于区分 HCC 和 LM 具有更高的准确性。
