Contrast-Enhanced Ultrasound with Deep Learning with Attention Mechanisms for Predicting Microvascular Invasion in Single Hepatocellular Carcinoma

Abstract

Rationale and objectives: Prediction of microvascular invasion (MVI) status of hepatocellular carcinoma (HCC) holds clinical significance for decision-making regarding the treatment strategy and evaluation of patient prognosis. We developed a deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict MVI of HCC.

Materials and methods: We retrospectively analyzed the data for single primary HCCs that were evaluated with CEUS 1 week before surgical resection from December 2014 to February 2022. The study population was divided into training (n = 198) and test (n = 54) cohorts. In this study, three DL models (Resnet50, Resnet50+BAM, Resnet50+SE) were trained using the training cohort and tested in the test cohort. Tumor characteristics were also evaluated by radiologists, and multivariate regression analysis was performed to determine independent indicators for the development of predictive nomogram models. The performance of the three DL models was compared to that of the MVI prediction model based on radiologist evaluations.

Results: The best-performing model, ResNet50+SE model achieved the ROC of 0.856, accuracy of 77.2, specificity of 93.9%, and sensitivity of 52.4% in the test group. The MVI prediction model based on a combination of three independent predictors showed a C-index of 0.729, accuracy of 69.4, specificity of 73.8%, and sensitivity of 62%.

Conclusion: The DL algorithm can accurately predict MVI of HCC on the basis of CEUS images, to help identify high-risk patients for the assist treatment.

Keywords: Contrast-enhanced ultrasound; Deep learning; Hepatocellular carcinoma; Microvascular invasion; Single.