MRI and Artificial Intelligence in Oncology: Current Research and Future Directions
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 57
Special Issue Editors
Interests: radiology; oncology; cancer; MRI; AI; imaging biomarkers
Special Issue Information
Dear Colleagues,
Data generation for diagnostic quality MRI across organs has been standardized using established radiology guidelines. However, challenges remain regarding how best to integrate new artificial intelligence (AI) methods with MRI. Radiologists, imaging scientists, and MRI technologists need to work together to implement MRI-AI methods for clinical oncological use. In order to make progress and obtain the required number of curated datasets for training MRI-AI methods, these methods must become part of everyday radiology and/or oncology clinical workflows.
To maximize the use of MRI-AI methods in the future, the following aims should be considered: (i) Radiology should embrace the needs of oncology for MRI-derived metrics like total tumor volume, which goes beyond the standard linear size measurement of a lesion. (ii) The curation of MR images should be integrated into radiology workflows without impeding productivity, which may eventually become part of fully automated AI systems. (iii) Longitudinal tracking and extraction of MRI metrics from registered lesions is productive for the evaluation of treatment response. (iv) Finally, analysis of big MRI data tied to additional data from clinical trial analysis is expected to create a data-driven taxonomy of cancer, optimize treatment decisions, and improve cancer prognosis.
Prof. Dr. Amita Shukla-Dave
Dr. Ramesh Paudyal
Guest Editors
Manuscript Submission Information
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Keywords
- MRI
- AI
- radiology
- oncology
- clinical trials
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