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Cancers
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Magnetic Resonance Imaging of Brain Tumor
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Magnetic Resonance Imaging of Brain Tumor

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 2583

Special Issue Editors

Prof. Dr. Amir Samii

E-Mail Website
Guest Editor
Department of Neurosurgery, INI-Hannover, 30625 Hannover, Germany
Interests: microneurosurgery; neuroendoscopy; intraoperative MRI; neuronavigation; minimally invasive procedures
Prof. Dr. Heinrich Lanfermann

E-Mail Website
Guest Editor
Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, 30625 Hannover, Germany
Interests: cat12 toolbox; clinically applicable qmri brain grey matter measurement; healthy ageing brain grey matter; neurodegenerative disease diagnosis; ALS
Dr. Peter Raab

E-Mail Website
Guest Editor
Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, 30625 Hannover, Germany
Interests: MRI; imaging; brain

Special Issue Information

Dear Colleagues,

The fifth edition of the WHO classification of tumors of the central nervous system (CNS) impressively documents the importance of advanced molecular diagnostics and has established a new CNS tumor nomenclature, with new tumor types and grading schemes. Accordingly, neurosurgeons and neuroradiologists have to face the challenge of using this new classification for improved preoperative MRI diagnostics and neurosurgical planning for safe and efficient interventions. Advanced MRI techniques such as MR perfusion and MR spectroscopy were already suitable for identifying regions of focal dedifferentiation within tumor tissue. With the current knowledge that several gene defects have an impact on tumor development and growth, more advanced techniques such as microstructural as well as quantitative susceptibility weighted imaging or a set of selected MR sequences in combination with artificial intelligence (AI) analysis—so-called radiogenomics—may have to be applied in order to improve the already impressive accuracy of diagnosing a brain tumor and to determine the nature (benign vs. malignant) and maybe the tumor type and tumor border. The important spatial relation of neurologically relevant structures and pathological tissue is increasingly identified via functional imaging using BOLD and DTI sequences.  

Imaging is now confronted with several questions: what is the capability of the most advanced MRI techniques, including the option of AI regarding the determination of brain tumor types, their characteristics according to the fifth edition of WHO classification, preoperative planning for tumor border identification, the preservation of the so-called eloquent areas, monitoring of brain tumor therapy, and early diagnosis of recurrent tumor?

To answer these questions, we would like to invite you to submit the results of recent studies using cutting-edge MRI techniques.

Prof. Dr. Amir Samii
Prof. Dr. Heinrich Lanfermann
Dr. Peter Raab
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. 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

  • central nervous system (CNS) tumor
  • MRI
  • radiogenomics
  • functional imaging
  • artificial intelligence

Published Papers (3 papers)

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Research

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17 pages, 8313 KiB  
Article
Automatic Brain Tissue and Lesion Segmentation and Multi-Parametric Mapping of Contrast-Enhancing Gliomas without the Injection of Contrast Agents: A Preliminary Study
by Jing Liu, Angela Jakary, Javier E. Villanueva-Meyer, Nicholas A. Butowski, David Saloner, Jennifer L. Clarke, Jennie W. Taylor, Nancy Ann Oberheim Bush, Susan M. Chang, Duan Xu and Janine M. Lupo
Cancers 2024, 16(8), 1524; https://doi.org/10.3390/cancers16081524 - 17 Apr 2024
Viewed by 457
Abstract
This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 [...] Read more.
This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated. Full article
(This article belongs to the Special Issue Magnetic Resonance Imaging of Brain Tumor)
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23 pages, 6788 KiB  
Article
Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study)
by Andreas Stadlbauer, Katarina Nikolic, Stefan Oberndorfer, Franz Marhold, Thomas M. Kinfe, Anke Meyer-Bäse, Diana Alina Bistrian, Oliver Schnell and Arnd Doerfler
Cancers 2024, 16(6), 1102; https://doi.org/10.3390/cancers16061102 - 8 Mar 2024
Viewed by 1211
Abstract
The mutational status of the isocitrate dehydrogenase (IDH) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of [...] Read more.
The mutational status of the isocitrate dehydrogenase (IDH) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of oxygen metabolism and tissue hypoxia as well as associated neovascularization and microvascular architecture. However, evaluating such complex neuroimaging data requires computational support. Traditional machine learning algorithms and simple deep learning models were trained with radiomic features from clinical MRI (cMRI) or physio-metabolic MRI data. A total of 215 patients (first center: 166 participants + 16 participants for independent internal testing of the algorithms versus second site: 33 participants for independent external testing) were enrolled using two different physio-metabolic MRI protocols. The algorithms trained with physio-metabolic data demonstrated the best classification performance in independent internal testing: precision, 91.7%; accuracy, 87.5%; area under the receiver operating curve (AUROC), 0.979. In external testing, traditional machine learning models trained with cMRI data exhibited the best IDH classification results: precision, 84.9%; accuracy, 81.8%; and AUROC, 0.879. The poor performance for the physio-metabolic MRI approach appears to be explainable by site-dependent differences in data acquisition methodologies. The physio-metabolic MRI approach potentially supports reliable classification of IDH gene status in the presurgical stage of glioma patients. However, non-standardized protocols limit the level of evidence and underlie the need for a reproducible framework of data acquisition techniques. Full article
(This article belongs to the Special Issue Magnetic Resonance Imaging of Brain Tumor)
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Review

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11 pages, 556 KiB  
Review
Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review
by Vivien Richter, Ulrike Ernemann and Benjamin Bender
Cancers 2024, 16(10), 1792; https://doi.org/10.3390/cancers16101792 - 8 May 2024
Viewed by 377
Abstract
The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. [...] Read more.
The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed. Full article
(This article belongs to the Special Issue Magnetic Resonance Imaging of Brain Tumor)
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