Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.2
Journals
Cancers
Special Issues
Critical Advances in the Diagnosis and Treatment of High-Grade Gliomas
share announcement

Critical Advances in the Diagnosis and Treatment of High-Grade Gliomas

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: 17 October 2024 | Viewed by 8968

Special Issue Editors

Dr. Timothy R. Smith

E-Mail Website
Guest Editor
Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: brain tumors; neurosurgery; digital phenotyping; pituitary tumors; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Joshua D. Bernstock

E-Mail Website
Guest Editor
Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: brain malignancies

Special Issue Information

Dear Colleagues,

Outcomes for pediatric and adult patients with high-grade gliomas remain dismal, despite aggressive surgical resection(s) and/or adjuvant radiation/chemotherapy. Despite extensive molecular and cellular characterization, these tumors continue to be the most lethal solid primary neoplasm within the central nervous system. These poor clinical outcomes are driven, in part, by tumor heterogeneity, genetic mutations/alterations, and immunosuppressive tumor microenvironments.

Accordingly, this Special Issue will attempt to highlight recent advances in the field, ranging from early preclinical to clinical. We also encourage the submission of translational papers in both the therapeutic and diagnostic space and are eager to review those, with a focus on novel targeted therapies and/or immunotherapies.

We also encourage clinicians to submit articles describing innovative diagnostic modalities (e.g., both molecular and radiologic) and welcome analyses centered on clinical data. Surgeons are encouraged to submit their experiences related to resection, the use of advanced imaging, and/or the delivery of local experimental therapeutics.

Finally, pertinent reviews related to such topics are also encouraged, in an effort to summarize the current status in the field and future directions.

Dr. Timothy R. Smith
Dr. Joshua D. Bernstock
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. 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

  • high-grade gliomas (HGG)
  • neurosurgery
  • neuro-oncology
  • experimental therapeutics
  • immunotherapy
  • diagnostics
  • clinical outcomes
  • translational research

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

14 pages, 3458 KiB  
Article
Risk Estimation in Non-Enhancing Glioma: Introducing a Clinical Score
by Philip Dao Trong, Samuel Kilian, Jessica Jesser, David Reuss, Fuat Kaan Aras, Andreas Von Deimling, Christel Herold-Mende, Andreas Unterberg and Christine Jungk
Cancers 2023, 15(9), 2503; https://doi.org/10.3390/cancers15092503 - 27 Apr 2023
Viewed by 1407
Abstract
The preoperative grading of non-enhancing glioma (NEG) remains challenging. Herein, we analyzed clinical and magnetic resonance imaging (MRI) features to predict malignancy in NEG according to the 2021 WHO classification and developed a clinical score, facilitating risk estimation. A discovery cohort (2012–2017, n [...] Read more.
The preoperative grading of non-enhancing glioma (NEG) remains challenging. Herein, we analyzed clinical and magnetic resonance imaging (MRI) features to predict malignancy in NEG according to the 2021 WHO classification and developed a clinical score, facilitating risk estimation. A discovery cohort (2012–2017, n = 72) was analyzed for MRI and clinical features (T2/FLAIR mismatch sign, subventricular zone (SVZ) involvement, tumor volume, growth rate, age, Pignatti score, and symptoms). Despite a “low-grade” appearance on MRI, 81% of patients were classified as WHO grade 3 or 4. Malignancy was then stratified by: (1) WHO grade (WHO grade 2 vs. WHO grade 3 + 4) and (2) molecular criteria (IDHmut WHO grade 2 + 3 vs. IDHwt glioblastoma + IDHmut astrocytoma WHO grade 4). Age, Pignatti score, SVZ involvement, and T2/FLAIR mismatch sign predicted malignancy only when considering molecular criteria, including IDH mutation and CDKN2A/B deletion status. A multivariate regression confirmed age and T2/FLAIR mismatch sign as independent predictors (p = 0.0009; p = 0.011). A “risk estimation in non-enhancing glioma” (RENEG) score was derived and tested in a validation cohort (2018–2019, n = 40), yielding a higher predictive value than the Pignatti score or the T2/FLAIR mismatch sign (AUC of receiver operating characteristics = 0.89). The prevalence of malignant glioma was high in this series of NEGs, supporting an upfront diagnosis and treatment approach. A clinical score with robust test performance was developed that identifies patients at risk for malignancy. Full article
(This article belongs to the Special Issue Critical Advances in the Diagnosis and Treatment of High-Grade Gliomas)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

12 pages, 844 KiB  
Review
Cerebellar High-Grade Glioma: A Translationally Oriented Review of the Literature
by Ashley L. B. Raghu, Jason A. Chen, Pablo A. Valdes, Walid Ibn Essayed, Elizabeth Claus, Omar Arnaout, Timothy R. Smith, E. Antonio Chiocca, Pier Paolo Peruzzi and Joshua D. Bernstock
Cancers 2023, 15(1), 174; https://doi.org/10.3390/cancers15010174 - 28 Dec 2022
Cited by 2 | Viewed by 2337
Abstract
World Health Organization (WHO) grade 4 gliomas of the cerebellum are rare entities whose understanding trails that of their supratentorial counterparts. Like supratentorial high-grade gliomas (sHGG), cerebellar high-grade gliomas (cHGG) preferentially affect males and prognosis is bleak; however, they are more common in [...] Read more.
World Health Organization (WHO) grade 4 gliomas of the cerebellum are rare entities whose understanding trails that of their supratentorial counterparts. Like supratentorial high-grade gliomas (sHGG), cerebellar high-grade gliomas (cHGG) preferentially affect males and prognosis is bleak; however, they are more common in a younger population. While current therapy for cerebellar and supratentorial HGG is the same, recent molecular analyses have identified features and subclasses of cerebellar tumors that may merit individualized targeting. One recent series of cHGG included the subclasses of (1) high-grade astrocytoma with piloid features (HGAP, ~31% of tumors); (2) H3K27M diffuse midline glioma (~8%); and (3) isocitrate dehydrogenase (IDH) wildtype glioblastoma (~43%). The latter had an unusually low-frequency of epidermal growth factor receptor (EGFR) and high-frequency of platelet-derived growth factor receptor alpha (PDGFRA) amplification, reflecting a different composition of methylation classes compared to supratentorial IDH-wildtype tumors. These new classifications have begun to reveal insights into the pathogenesis of HGG in the cerebellum and lead toward individualized treatment targeted toward the appropriate subclass of cHGG. Emerging therapeutic strategies include targeting the mitogen-activated protein kinases (MAPK) pathway and PDGFRA, oncolytic virotherapy, and immunotherapy. HGGs of the cerebellum exhibit biological differences compared to sHGG, and improved understanding of their molecular subclasses has the potential to advance treatment. Full article
(This article belongs to the Special Issue Critical Advances in the Diagnosis and Treatment of High-Grade Gliomas)
►▼ Show Figures

Figure 1

21 pages, 1411 KiB  
Review
Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review
by Alexander G. Yearley, Sarah E. Blitz, Ruchit V. Patel, Alvin Chan, Lissa C. Baird, Gregory K. Friedman, Omar Arnaout, Timothy R. Smith and Joshua D. Bernstock
Cancers 2022, 14(22), 5608; https://doi.org/10.3390/cancers14225608 - 15 Nov 2022
Cited by 1 | Viewed by 2176
Abstract
Background: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. [...] Read more.
Background: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. Methods: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. Results: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. Conclusions: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake. Full article
(This article belongs to the Special Issue Critical Advances in the Diagnosis and Treatment of High-Grade Gliomas)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

22 pages, 2321 KiB  
Systematic Review
Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review
by Mehnaz Tabassum, Abdulla Al Suman, Eric Suero Molina, Elizabeth Pan, Antonio Di Ieva and Sidong Liu
Cancers 2023, 15(15), 3845; https://doi.org/10.3390/cancers15153845 - 28 Jul 2023
Cited by 3 | Viewed by 2325
Abstract
Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about [...] Read more.
Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors’ features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor’s genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients’ prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other—tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats. Full article
(This article belongs to the Special Issue Critical Advances in the Diagnosis and Treatment of High-Grade Gliomas)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop