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Diagnostics
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Research Update on Magnetic Resonance Imaging
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Research Update on Magnetic Resonance Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 2761

Special Issue Editor

Dr. Douglas Kelley

E-Mail Website
Guest Editor
GE Corporate, Consultant, Fairfax, CA, USA
Interests: magnetic resonance imaging; MRI

Special Issue Information

Dear Colleagues,

This "Research Update on Magnetic Resonance Imaging" presents the latest advancements and research findings in the field of magnetic resonance imaging (MRI). This special edition highlights innovative techniques, emerging applications, and the latest in MRI technology, aiming to push the boundaries of medical imaging. Our contributors, including some leading experts in the field, offer insights into the current state of MRI research and its future prospects, making this a must-read for researchers, clinicians, and anyone interested in the latest developments in MRI.

Dr. Douglas Kelley
Guest Editor

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 short 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. Diagnostics 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 2600 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

  • magnetic resonance imaging
  • MRI
  • diagnosis
  • artificial intelligence
  • machine learning
  • deep learning
  • radiology
  • imaging

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Published Papers (2 papers)

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10 pages, 4449 KiB  
Article
Qualitative Magnetic Resonance Imaging Assessment of the Semimembranosus Tendon in Patients with Medial Meniscal Tears
by Haron Obaid, Adarsh Patel, Emily McWalter, Mark Ernst, Prosanta Mondal and Michael L. Shepel
Diagnostics 2024, 14(17), 1962; https://doi.org/10.3390/diagnostics14171962 - 5 Sep 2024
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Abstract
Background: To determine if there is an association between semimembranosus tendinosis and medial meniscal tears using MRI. Methods: A retrospective review of knee 3T MRI scans was performed to determine the presence or absence of medial meniscal tears in patients with semimembranosus tendinosis. [...] Read more.
Background: To determine if there is an association between semimembranosus tendinosis and medial meniscal tears using MRI. Methods: A retrospective review of knee 3T MRI scans was performed to determine the presence or absence of medial meniscal tears in patients with semimembranosus tendinosis. All studies were interpreted by two musculoskeletal radiologists. Univariate association for the presence of semimembranosus tendinosis and medial meniscal tears was performed with a Chi-square test followed by logistic regression modelling among statistically significant associations. Results: A total of 150 knee MRI scans were reviewed (age 32.8 ± 7.1 years; 70 females). Semimembranosus tendinosis was present in 66 knees (44%) in the patient population. Semimembranosus tendinosis was present in 81% of patients with meniscal tears versus 36% of patients without meniscal tears (p < 0.0001). This association remained statistically significant when adjusted for age and sex with an adjusted odds ratio of 7.0 (p < 0.0003). Models adjusted for the above covariates and containing the interaction term produced an adjusted odds ratio of 13.0 (p < 0.0001) in men, while in women this association was non-significant with an adjusted odds ratio of 2.0 (p = 0.42). Conclusions: Subjects with semimembranosus tendinosis were seven times more likely to have medial meniscal tears even when adjusting for sex and age. This could help guide the appropriate postmeniscal repair rehabilitation protocol. Full article
(This article belongs to the Special Issue Research Update on Magnetic Resonance Imaging)
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11 pages, 468 KiB  
Systematic Review
Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review
by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila and Prakashini Koteshwar
Diagnostics 2024, 14(23), 2741; https://doi.org/10.3390/diagnostics14232741 - 5 Dec 2024
Cited by 2 | Viewed by 1419
Abstract
Background: Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and [...] Read more.
Background: Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies. Methodology: A systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. Results: A systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. Conclusions: The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community. Full article
(This article belongs to the Special Issue Research Update on Magnetic Resonance Imaging)
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