Radiomics and liver: Where we are and where we are headed?

Cesare Maino, Federica Vernuccio,Roberto Cannella,Paolo Niccolo Franco, Valentina Giannini,Michele Dezio,Antonio Rosario Pisani, Antonino Andrea Blandino,Riccardo Faletti, Elisabetta De Bernardi,Davide Ippolito,Marco Gatti,Riccardo Inchingolo

EUROPEAN JOURNAL OF RADIOLOGY(2024)

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摘要
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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关键词
Hepatocellular carcinoma,Cholangiocarcinoma,Artificial Intelligence,Machine Learning,Deep Learning,Liver metastases,Magnetic resonance imaging,Computed Tomography,Radiomics,Texture analysis,Prognosis,Diagnosis
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