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.
Key Words
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Article info
Publication history
Published online: December 23, 2022
Accepted:
December 2,
2022
Received in revised form:
November 29,
2022
Received:
October 29,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
