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Diagnosis, Therapy, and Healthcare for Patients with Glioma
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Diagnosis, Therapy, and Healthcare for Patients with Glioma

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 (30 March 2021) | Viewed by 6947

Special Issue Editors

PD Dr. Cornelia Brendle

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Guest Editor
Department of Radiology, University of Tübingen, Tübingen, Germany
Interests: brain tumors; MRI; hybrid imaging; neuroradiology; brown adipose tissue
PD Dr. Mirjam Renovanz

E-Mail Website
Guest Editor
Department of Neurology & Interdisciplinary Neuro-Oncology
Department of Neurosurgery
Hertie Institute for Clinical Brain Research
University Hospital Tübingen, Germany
Eberhard Karls University Tübingen
Interests: brain tumor; neuro-oncology; quality of life; psychosocial assessment; patient-doctor communication

Special Issue Information

Dear Colleagues,

We invite authors to submit manuscripts for our Special Issue on the diagnosis, therapy, and healthcare for patients with gliomas. Welcome is all original work focusing on clinical management of glioma patients.

The issue will cover current diagnostic and therapeutic strategies as well as future developments. Innovative imaging techniques, including advanced MRI and PET and image data analysis by artificial intelligence, have shown benefit in the initial diagnosis of gliomas and the course of the disease. However, clinical applicability is not clarified in detail. During the last few years, the treatment options for patients harboring gliomas, the beneficial effects of surgical resection, and new approaches in diagnostic technique have been evolving. However, in most patients, the prognosis remains limited. New prognostic markers in pathologic examination or diagnostic imaging might help to improve the management of glioma patients.

PD Dr. Cornelia Brendle
PD Dr. Mirjam Renovanz
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 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

  • Glioma
  • Diagnostic imaging
  • MRI
  • Prognostic marker
  • Therapy management
  • Adequate health care for glioma patients

Published Papers (3 papers)

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Research

11 pages, 1692 KiB  
Article
ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging
by Nils C. Nuessle, Felix Behling, Ghazaleh Tabatabai, Salvador Castaneda Vega, Jens Schittenhelm, Ulrike Ernemann, Uwe Klose and Johann-Martin Hempel
J. Clin. Med. 2021, 10(16), 3451; https://doi.org/10.3390/jcm10163451 - 4 Aug 2021
Cited by 8 | Viewed by 1775
Abstract
Purpose: To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades. Materials and methods: Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, [...] Read more.
Purpose: To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades. Materials and methods: Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC). Results: Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively). Conclusions: High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol. Full article
(This article belongs to the Special Issue Diagnosis, Therapy, and Healthcare for Patients with Glioma)
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11 pages, 2694 KiB  
Article
Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging
by Johann-Martin Hempel, Cornelia Brendle, Sasan Darius Adib, Felix Behling, Ghazaleh Tabatabai, Salvador Castaneda Vega, Jens Schittenhelm, Ulrike Ernemann and Uwe Klose
J. Clin. Med. 2021, 10(11), 2325; https://doi.org/10.3390/jcm10112325 - 26 May 2021
Cited by 7 | Viewed by 2603
Abstract
Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous [...] Read more.
Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. Methods: Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 1 August 2013 and 30 October 2017. The area on scatter plots with a specific combination of MK and MD values, not occurring in the healthy brain, was labeled, and the corresponding voxels were visualized on the fluid-attenuated inversion recovery (FLAIR) images. Reversely, the labeled voxels were compared to those of the manually segmented tumor volume, and the Dice similarity coefficient was used to investigate their spatial overlap. Results: A specific combination of MK and MD values in whole-brain DKI maps, visualized on a two-dimensional scatter plot, exclusively occurs in glioma tissue including the perifocal infiltrative zone and is absent in tissue of the normal brain or from other intracranial compartments. Conclusions: A unique diffusion signature with a specific combination of MK and MD values from whole-brain DKI can identify diffuse glioma without any previous segmentation. This feature might influence artificial intelligence algorithms for automatic tumor segmentation and provide new aspects of tumor heterogeneity. Full article
(This article belongs to the Special Issue Diagnosis, Therapy, and Healthcare for Patients with Glioma)
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11 pages, 1012 KiB  
Article
Dynamic Susceptibility Perfusion Imaging for Differentiating Progressive Disease from Pseudoprogression in Diffuse Glioma Molecular Subtypes
by Vivien Richter, Uwe Klose, Benjamin Bender, Katharina Rabehl, Marco Skardelly, Jens Schittenhelm, Ghazaleh Tabatabai, Johann-Martin Hempel, Ulrike Ernemann and Cornelia Brendle
J. Clin. Med. 2021, 10(4), 598; https://doi.org/10.3390/jcm10040598 - 5 Feb 2021
Cited by 3 | Viewed by 2171
Abstract
Rationale and Objectives: Advanced adjuvant therapy of diffuse gliomas can result in equivocal findings in follow-up imaging. We aimed to assess the additional value of dynamic susceptibility perfusion imaging in the differentiation of progressive disease (PD) from pseudoprogression (PsP) in different molecular glioma [...] Read more.
Rationale and Objectives: Advanced adjuvant therapy of diffuse gliomas can result in equivocal findings in follow-up imaging. We aimed to assess the additional value of dynamic susceptibility perfusion imaging in the differentiation of progressive disease (PD) from pseudoprogression (PsP) in different molecular glioma subtypes. Materials and Methods: 89 patients with treated diffuse glioma with different molecular subtypes (IDH wild type (Astro-IDHwt), IDH mutant astrocytomas (Astro-IDHmut) and oligodendrogliomas), and tumor-suspect lesions on post-treatment follow-up imaging were classified into two outcome groups (PD or PsP) retrospectively by histopathology or clinical follow-up. The relative cerebral blood volume (rCBV) was assessed in the tumor-suspect FLAIR and contrast-enhancing (CE) lesions. We analyzed how a multilevel classification using a molecular subtype, the presence of a CE lesion, and two rCBV histogram parameters performed for PD prediction compared with a decision tree model (DTM) using additional rCBV parameters. Results: The PD rate was 69% in the whole cohort, 86% in Astro-IDHwt, 52% in Astro-IDHmut, and 55% in oligodendrogliomas. In the presence of a CE lesion, the PD rate was higher with 82%, 94%, 59%, and 88%, respectively; if there was no CE lesion, however, the PD rate was only 44%, 60%, 40%, and 33%, respectively. The additional use of the rCBV parameters in the DTM yielded a prediction accuracy for PD of 99%, 100%, 93%, and 95%, respectively. Conclusion: Utilizing combined information about the molecular tumor type, the presence or absence of CE lesions and rCBV parameters increases PD prediction accuracy in diffuse glioma. Full article
(This article belongs to the Special Issue Diagnosis, Therapy, and Healthcare for Patients with Glioma)
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