Artificial Intelligence and MRI Characterization of Tumors: 2nd Edition
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 11 December 2024 | Viewed by 2427
Special Issue Editors
Interests: diagnostic imaging and interventional radiology oncology; diagnostic imaging and interventional vascular radiology; musculosheletal diagnostics and intervention; urogynecological diagnostics; computed tomography; magnetic resonance imaging; ultrasound; radiomics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: AI; machine learning and big data analytics with applications to data signals; 2D and 3D image and video processing and analysis
Special Issues, Collections and Topics in MDPI journals
2. Department of Radiology, Sant'Anna Hospital, 22100 San Fermo della Battaglia, CO, Italy
Interests: breast diagnostics; prostate diagnostics; gynecological diagnostics; mammography; magnetic resonance imaging; artificial intelligence; radiomics; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine learning; deep learning; medical imaging; precision medicine; radiomics; multimodal learning; decision support systems; federated learning; smart devices
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is the second edition of “Artificial Intelligence and MRI Characterization of Tumors”, available at https://www.mdpi.com/journal/cancers/special_issues/AIAMCOT.
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 that aid in the support of medical decisions. This kind of analysis allows the extraction of many 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 utilizes fewer resources to improve the overall efficiency and accuracy of cancer diagnosis and management.
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
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- cancer
- MRI
- CT
- PET 18F-FDG
- PET/CT scintigraphy
- artificial intelligence (AI)
- radiomics convolutional neural networks (CNN)
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