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Cancers
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MRI and Artificial Intelligence in Oncology: Current Research and Future...
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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 209

Special Issue Editors

Prof. Dr. Amita Shukla-Dave

E-Mail Website
Guest Editor
Departments of Medical Physics and Radiology, Memorial Sloan-Kettering Cancer Center (MSKCC), New York, NY, USA
Interests: radiology; oncology; cancer; MRI; AI; imaging biomarkers
Dr. Ramesh Paudyal

E-Mail Website
Guest Editor
Department of Medical Physics, Memorial Sloan-Kettering Cancer Center (MSKCC), New York, NY, USA
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MRI
  • AI
  • radiology
  • oncology
  • clinical trials

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Published Papers (1 paper)

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Research

14 pages, 2941 KiB  
Article
Correction of Gradient Nonlinearity Bias in Apparent Diffusion Coefficient Measurement for Head and Neck Cancers Using Single- and Multi-Shot Echo Planar Diffusion Imaging
by Ramesh Paudyal, Alfonso Lema-Dopico, Akash Deelip Shah, Vaios Hatzoglou, Muhammad Awais, Eric Aliotta, Victoria Yu, Thomas L. Chenevert, Dariya I. Malyarenko, Lawrence H. Schwartz, Nancy Lee and Amita Shukla-Dave
Cancers 2025, 17(11), 1796; https://doi.org/10.3390/cancers17111796 - 28 May 2025
Viewed by 92
Abstract
Background/Objectives: This work prospectively evaluates the vendor-provided Low Variance (LOVA) apparent diffusion coefficient (ADC) gradient nonlinearity correction (GNC) technique for primary tumors, neck nodal metastases, and normal masseter muscles in patients with head and neck cancers (HNCs). Methods: Multiple b-value diffusion-weighted (DW)-MR [...] Read more.
Background/Objectives: This work prospectively evaluates the vendor-provided Low Variance (LOVA) apparent diffusion coefficient (ADC) gradient nonlinearity correction (GNC) technique for primary tumors, neck nodal metastases, and normal masseter muscles in patients with head and neck cancers (HNCs). Methods: Multiple b-value diffusion-weighted (DW)-MR images were acquired on a 3.0 T scanner using a single-shot echo planar imaging (SS-EPI) and multi-shot (MS)-EPI for diffusion phantom materials (20% and 40% polyvinylpyrrolidone (PVP) in water). Pretreatment DW-MRI acquisitions were performed for sixty HNC patients (n = 60) who underwent chemoradiation therapy. ADC values with and without GNC were calculated offline using a monoexponential diffusion model over all b-values, relative percentage (r%) changes (Δ) in ADC values with and without GNC were calculated, and the ADC histograms were analyzed. Results: Mean ADC values calculated using SS-EPI DW data with and without GNC differed by ≤1% for both PVP20% and PVP40% at the isocenter, whereas off-center differences were ≤19.6% for both concentrations. A similar trend was observed for these materials with MS-EPI. In patients, the mean rΔADC (%) values measured with SS-EPI differed by 4.77%, 3.98%, and 5.68% for primary tumors, metastatic nodes, and masseter muscle. MS-EPI exhibited a similar result with 5.56%, 3.95%, and 4.85%, respectively. Conclusions: This study showed that the GNC method improves the robustness of the ADC measurement, enhancing its value as a quantitative imaging biomarker used in HNC clinical trials. Full article
(This article belongs to the Special Issue MRI and Artificial Intelligence in Oncology: Current Research and Future Directions)
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