Xiao Meiqin, Deng Yishu, Zheng Wei, Huang Lishu, Wang Wei, Yang Hao, Gao Danyan, Guo Zhixing, Wang Jianwei, Li Chaofeng, Li Fang, Han Feng
Department of Ultrasound, Sun Yat-sen University Cancer Center, Guangzhou, China.
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Heliyon. 2024 Oct 2;10(20):e38850. doi: 10.1016/j.heliyon.2024.e38850. eCollection 2024 Oct 30.
To enhance the accuracy of hepatocellular carcinoma (HCC) diagnosis using contrast-enhanced (CE) US, the American College of Radiology developed the CEUS Liver Imaging Reporting and Data System (LI-RADS). However, the system still exhibits limitations in distinguishing between HCC and non-HCC lesions.
To investigate the viability of employing machine learning methods based on quantitative parameters of contrast-enhanced ultrasound for distinguishing HCC within LR-M nodules.
This retrospective analysis was conducted on pre-treatment CEUS data from liver nodule patients across multiple centers between January 2013 and June 2022. Quantitative analysis was performed using CEUS images, and the machine learning diagnostic models based on quantitative parameters were utilized for the classification diagnosis of LR-M nodules. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) and compared with the performance of four radiologists.
The training and internal testing datasets comprised 168 patients (median age, 53 years [IQR, 18 years]), while the external testing datasets from two other centers included 110 patients (median age, 54 years [IQR, 16 years]). In the internal independent test set, the top-performing Random Forest model achieved an AUC of 0.796 (95%CI: 0.729-0.853) for diagnosing HCC. This model exhibited a sensitivity of 0.752 (95%CI: 0.750-0.755) and a specificity of 0.761 (95%CI: 0.758-0.764), outperforming junior radiologists who achieved an AUC of 0.619 (95%CI: 0.543-0.691, < .01) with sensitivity and specificity of 0.716 (95%CI: 0.713-0.718) and 0.522 (95%CI: 0.519-0.526), respectively.
Significant differences in contrast-enhanced ultrasound quantitative parameters are observed between HCC and non-HCC lesions. Machine learning models leveraging these parameters effectively distinguish HCC categorized as LR-M, offering a valuable adjunct for the accurate classification of liver nodules within the CEUS LI-RADS framework.
为提高使用对比增强超声（CE US）诊断肝细胞癌（HCC）的准确性，美国放射学会制定了CEUS肝脏影像报告和数据系统（LI-RADS）。然而，该系统在区分HCC和非HCC病变方面仍存在局限性。
探讨基于对比增强超声定量参数的机器学习方法在鉴别LR-M类结节内HCC的可行性。
对2013年1月至2022年6月期间多个中心的肝脏结节患者的治疗前CEUS数据进行回顾性分析。使用CEUS图像进行定量分析，并利用基于定量参数的机器学习诊断模型对LR-M类结节进行分类诊断。采用受试者操作特征曲线下面积（AUC）评估模型性能，并与四位放射科医生的表现进行比较。
训练数据集和内部测试数据集包括168例患者（中位年龄53岁[四分位间距，18岁]），而来自其他两个中心的外部测试数据集包括110例患者（中位年龄54岁[四分位间距，16岁]）。在内部独立测试集中，表现最佳的随机森林模型诊断HCC的AUC为0.796（95%CI：0.729-0.853）。该模型的灵敏度为0.752（95%CI：0.750-0.755）, 特异度为0.761（95%CI：0.758-0.764），优于初级放射科医生，后者的AUC为0.619（95%CI：0.543-0.691，P<0.01），灵敏度和特异度分别为0.716（95%CI：0.713-0.718）和0.522（95%CI：0.519-0.526）。
HCC与非HCC病变在对比增强超声定量参数上存在显著差异。利用这些参数的机器学习模型能有效鉴别归类为LR-M的HCC，为在CEUS LI-RADS框架内准确分类肝脏结节提供了有价值的辅助手段。
