Machine Learning and Radiomics Applications of MRI-Guided Treatments in Cancers
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6947
Special Issue Editor
Special Issue Information
Dear colleagues,
Advances in artificial intelligence and machine learning over the recent years including deep learning has spurred a lot of interest and applications of these techniques in several aspects of medicine including cancer diagnosis and treatment. Automated methods for analyzing medical images, including deep learning based detection and segmentation of cancers and normal organs, automated biomarkers for diagnosis and classification of cancer aggressiveness and prediction of outcomes will provide more reproducible and quantitative biomarkers to target and treat cancers. However, the automated analysis of biomedical images is generally restricted to computed tomography (CT) scans mostly due to their higher prevalence and use for cancer diagnosis and follow ups as well as the relative robustness of the scans to scanning variations compared to magnetic resonance imaging (MRI). Nevertheless, MRI provides higher soft-tissue contrast, thereby enabling better visualization of the tumors. MRI guidance is also a new technique for novel treatments like MRI-guided radiation therapy.
The purpose of this special issue is to highlight the advances in all aspects of machine learning, both deep learning and standard radiomics based machine learning applied to MRI for the purpose of robust detection, prognosis, prediction of response to cancer treatments. Submissions focusing on using MRI for cancer response assessment/prediction, improving robustness and reproducibility of MRI radiomics, classification or segmentation from MRI, and methods combining MRI with other imaging or non-imaging modalities are highly relevant.
Dr. Harini Veeraraghavan
Guest Editor
Manuscript Submission Information
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Keywords
- MRI guided treatments
- deep learning
- machine learning
- MRI harmonization
- radiomics
- MRI biomarkers
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