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Diagnostics
Special Issues
Clinical Advances and Applications in Neuroradiology
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Clinical Advances and Applications in Neuroradiology

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 901

Special Issue Editor

Dr. Houman Sotoudeh

E-Mail Website
Guest Editor
Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
Interests: neuroradiology; artificial intelligence; radiomics; neuro-oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neuroradiology is a rapidly advancing field of medicine that uses imaging techniques to diagnose and treat diseases of the head and neck, brain, spine, and nervous system. Clinical advances in neuroradiology have led to the development of new and improved imaging techniques, such as susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), resting-state and task-based functional MRI (fMRI), perfusion imaging (CT perfusion and MR perfusion), MR spectroscopy, and PET/ molecular imaging. These techniques have allowed neuroradiologists to evaluate the brain and nervous system in greater detail than ever before, which has led to improved diagnosis and treatment of a wide range of neurological disorders. Recently, artificial intelligence (AI) has gained strong momentum in clinical neuroradiology including, workflow optimization, image reconstruction, and AI-assisted diagnosis.

The aim of this Special Issue is to cover these advanced imaging techniques in clinical neuroradiology. We accept original research and reviews in this field.

Dr. Houman Sotoudeh
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

  • neuroradiology
  • advanced imaging
  • susceptibility-weighted imaging (SWI)
  • diffusion tensor imaging (DTI)
  • functional MRI (fMRI)
  • CT perfusion
  • MR perfusion
  • MR spectroscopy
  • PET
  • molecular imaging
  • artificial intelligence

Published Papers (2 papers)

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Research

13 pages, 720 KiB  
Article
Prediction of Seropositivity in Suspected Autoimmune Encephalitis by Use of Radiomics: A Radiological Proof-of-Concept Study
by Jacob Stake, Christine Spiekers, Burak Han Akkurt, Walter Heindel, Tobias Brix, Manoj Mannil and Manfred Musigmann
Diagnostics 2024, 14(11), 1070; https://doi.org/10.3390/diagnostics14111070 (registering DOI) - 21 May 2024
Abstract
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral [...] Read more.
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral segmentation of the amygdala was performed on pre-contrast T2 images using 3D Slicer open-source software. Our sample of 83 patients contained 43 seropositive and 40 seronegative AE cases. Images were obtained at our tertiary care center and at various secondary care centers in North Rhine-Westphalia, Germany. The sample was randomly split into training data and independent test data. A total of 107 radiomic features were extracted from bilateral regions of interest (ROIs). Automated machine learning (AutoML) was used to identify the most promising machine learning algorithms. Feature selection was performed using recursive feature elimination (RFE) and based on the determination of the most important features. Selected features were used to train various machine learning algorithms on 100 different data partitions. Performance was subsequently evaluated on independent test data. Our radiomics approach was able to predict the presence of autoantibodies in the independent test samples with a mean AUC of 0.90, a mean accuracy of 0.83, a mean sensitivity of 0.8,4 and a mean specificity of 0.82, with Lasso regression models yielding the most promising results. These results indicate that radiomics-based machine learning could be a promising tool in predicting the presence of autoantibodies in suspected AE patients. Given the implications of seropositivity for definitive diagnosis of suspected AE cases, this may expedite diagnostic workup even before results from specialized laboratory testing can be obtained. Furthermore, in conjunction with recent publications, our results indicate that characterization of AE subtypes by use of radiomics may become possible in the future, potentially allowing physicians to tailor treatment in the spirit of personalized medicine even before laboratory workup is completed. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
19 pages, 2295 KiB  
Article
Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform
by Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine and Houman Sotoudeh
Diagnostics 2024, 14(11), 1066; https://doi.org/10.3390/diagnostics14111066 (registering DOI) - 21 May 2024
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Abstract
Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and [...] Read more.
Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland–Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform’s low inter-user variability and the high accuracy of its T1c and FLAIR AI models. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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