Artificial Intelligence and MRI Characterization of Tumors

Dear Colleagues,

Cancer diagnosis and management remain complex and frequently require a multi-imaging assessment that allows for the staging of local and systemic disease. MRI is a highly accurate technique for the diagnosis and assessment of local disease extension, while CT, 18F-FDG PET/CT, and scintigraphy are often used for the confirmation of lymph node and systemic localization. Other laboratory, genetic, and histological parameters are essential to aid diagnosis, stratify risk, predict prognosis, and monitor patients during follow-up. However, many of these tools are susceptible to significant subjectivity.

In recent years, imaging-based machine learning processes, referred to as artificial intelligence, have been employed in many oncological fields, with promising results in the support of medical decisions. This kind of analysis allows the extraction of a large number of quantitative characteristics from medical images, called “features”, providing physicians with a valid decision-making tool. Using artificial intelligence algorithms reduces the degree of subjectivity and uses fewer resources to improve overall efficiency and accuracy in the diagnosis and management of cancer.

In this Special Issue, we intend to enclose a current and important chapter on the role of artificial intelligence applied to various types of imaging modalities, in all phases of cancer evaluation, from diagnosis, to therapy, to prognosis. Both types of traditional machine learning approaches will be examined: radionics analysis and convolutional neural networks.

Dr. Eliodoro Faiella
Dr. Paolo Soda
Dr. Domiziana Santucci
Dr. Ermanno Cordelli
Guest Editors

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