Work

The Capabilities of Machine Learning Radiomics Based Models in the MRI Diagnosis of Early HCC

Year: 2024

Type: article

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.

Source: Journal of oncology diagnostic radiology and radiotherapy

DOI: 10.37174/2587-7593-2024-7-4-68-73

Authors: I. V. Molostova, B. M. Medvedeva, E. V. Kondratyev, A. A. Ustalov, M. S. Novruzbekov, O. D. Olisov, V. M. Tarnoposky

Institutions: Russian Cancer Research Center NN Blokhin, Ministry of Health of the Russian Federation, National Medical Research Center of Surgery named after A. Vishnevsky, Research Institute of Emergency Care, N.V. Sklifosovsky Research Institute of Emergency Medicine

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4406327845

FWCI: 0.916

Cites: 20

Cited by: 4

Related to: 10