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Recent Advances in Clinical Neuroimaging
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Recent Advances in Clinical Neuroimaging

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Neurology".

Deadline for manuscript submissions: closed (25 February 2023) | Viewed by 7096

Special Issue Editor

Prof. Dr. Daoying Geng

E-Mail Website
Guest Editor
Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
Interests: magnetic resonance imaging; molecular imaging; medical diagnosis; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The nervous system is the dominant system in the human body that acts on physiological activities and external stimuli. Neuroimaging technology plays an important role in diagnosing nervous system-related diseases and analyzing brain microstructure and cognitive function. Imaging technologies currently used in clinic mainly include a variety of imaging methods based on CT and MR. With the development of artificial intelligence technology, a growing number of studies have begun to use image-based intelligent technology to solve practical clinical problems. AI has been successfully used in other fields, such as brain imaging or neuroimaging, to detect potential brain damage or tumors. Brain science is a major subject of scientific development in the world today. The pathophysiological mechanisms and clinical manifestations of neurological diseases are complex, and there are still many controversies in their basic and clinical theories. With the support of big data, high-performance computers and the Internet, AI medical imaging improves the level of medical imaging diagnoses, and gradually promotes the reform of medical imaging diagnosis mode; and together with the R&D and application of other medical disciplines, it will jointly promote the realization of the era of intelligent medicine.

Prof. Dr. Daoying Geng
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. Journal of Clinical Medicine 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

  • neuroimaging
  • brain imaging
  • MRI
  • CT
  • functional MRI
  • radiomics
  • deep learning
  • artificial intelligence

Published Papers (3 papers)

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Research

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10 pages, 2791 KiB  
Article
Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study
by Xin Cao, Yanwei Zeng, Junying Wang, Yunxi Cao, Yifan Wu and Wei Xia
J. Clin. Med. 2022, 11(13), 3623; https://doi.org/10.3390/jcm11133623 - 23 Jun 2022
Cited by 1 | Viewed by 1840
Abstract
The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation [...] Read more.
The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice. Full article
(This article belongs to the Special Issue Recent Advances in Clinical Neuroimaging)
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Review

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17 pages, 783 KiB  
Review
Progress in Brain Magnetic Resonance Imaging of Individuals with Prader–Willi Syndrome
by Zhongxin Huang and Jinhua Cai
J. Clin. Med. 2023, 12(3), 1054; https://doi.org/10.3390/jcm12031054 - 29 Jan 2023
Cited by 4 | Viewed by 2120
Abstract
Prader–Willi syndrome (PWS), a rare epigenetic disease mapping the imprinted chromosomal domain of 15q11.2-q13.3, manifests a regular neurodevelopmental trajectory in different phases. The current multimodal magnetic resonance imaging (MRI) approach for PWS focues on morphological MRI (mMRI), diffusion MRI (dMRI) and functional MRI [...] Read more.
Prader–Willi syndrome (PWS), a rare epigenetic disease mapping the imprinted chromosomal domain of 15q11.2-q13.3, manifests a regular neurodevelopmental trajectory in different phases. The current multimodal magnetic resonance imaging (MRI) approach for PWS focues on morphological MRI (mMRI), diffusion MRI (dMRI) and functional MRI (fMRI) to uncover brain alterations. This technique offers another perspective to understand potential neurodevelopmental and neuropathological processes of PWS, in addition to specific molecular gene expression patterns, various clinical manifestations and metabolic phenotypes. Multimodal MRI studies of PWS patients demonstrated common brain changes in the volume of gray matter, the integrity of the fiber tracts and the activation and connectivity of some networks. These findings mainly showed that brain alterations in the frontal reward circuit and limbic system were related to molecular genetics and clinical manifestations (e.g., overwhelming eating, obsessive compulsive behaviors and skin picking). Further exploration using a large sample size and advanced MRI technologies, combined with artificial intelligence algorithms, will be the main research direction to study the structural and functional changes and potential pathogenesis of PWS. Full article
(This article belongs to the Special Issue Recent Advances in Clinical Neuroimaging)
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23 pages, 730 KiB  
Review
A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas
by Peng Du, Hongyi Chen, Kun Lv and Daoying Geng
J. Clin. Med. 2022, 11(13), 3802; https://doi.org/10.3390/jcm11133802 - 30 Jun 2022
Cited by 3 | Viewed by 2376
Abstract
Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous [...] Read more.
Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas. Full article
(This article belongs to the Special Issue Recent Advances in Clinical Neuroimaging)
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