Machine learning model based on dynamic contrast-enhanced ultrasound assisting LI-RADS diagnosis of HCC: A
Meiqin Xiao1,2, Yishu Deng2,3,4, Wei Zheng1,2
1Department of Ultrasound, Sun Yat-sen University Cancer Center, Guangzhou, China.
Background:
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.
Purpose:
To investigate the viability of employing machine learning methods based on quantitative parameters of contrast-enhanced ultrasound for distinguishing HCC within LR-M nodules.
Materials And Methods:
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.
Results:
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, p < .01) with sensitivity and specificity of 0.716 (95%CI: 0.713-0.718) and 0.522 (95%CI: 0.519-0.526), respectively.
Conclusion:
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.
