Abstract
Purpose: To evaluate machine-learning radiomics based models on enhanced MR images in diagnostics of early HCC.Material and methods: Data from 72 patients with 93 masses who underwent Gadoxetic acid-enhanced MRI scans was retrospectively analyzed.Results: Binary classification models were produced for the differential diagnosis of regenerative and dysplastic nodes, early HCC and HCC nodes with an atypical enhancement with high discriminatory capabilities; the area under the ROC-curve ranged from 0.89 to 0.95 in various models.Conclusion: The performed radiomic models can be used as an effective method for differential diagnostics of HCC with typical and atypical enhancement, dysplastic and regenerative nodes.
Published Version
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