cancers-logo

Journal Browser

Journal Browser

Advances in Diagnostics and Treatments for Glioblastoma

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

Deadline for manuscript submissions: 10 April 2026 | Viewed by 876

Special Issue Editors


E-Mail Website
Guest Editor
Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Division of Clinical Neurooncology, University Medicine Essen, University Duisburg-Essen, Essen, Germany
Interests: glioblastoma; next-generation-sequencing; targeted therapy; deep learning; immunotherapy

E-Mail Website
Guest Editor
Department of Medical Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL 33176, USA
Interests: brain metastases; glioblastoma; clinical trial; neuro-oncology; glioma

Special Issue Information

Dear Colleagues,

Glioblastoma is the most common and aggressive primary brain tumor in adults, characterized by rapid progression, profound intratumoral heterogeneity, and resistance to therapy. Despite advances in surgery, radiotherapy, and systemic treatments, overall survival remains limited. Recent progress in imaging, molecular profiling, and biomarker discovery is transforming our diagnostic capabilities and enabling more precise, individualized therapeutic strategies.

For this Special Issue, we invite high-quality original research, clinical trial reports, and comprehensive reviews on innovations in both diagnostics and treatment in glioblastoma. We particularly welcome contributions addressing integrated diagnostic approaches, predictive biomarkers, novel imaging modalities, targeted therapies, immunotherapy, and multidisciplinary treatment strategies aimed at improving patient outcomes.

Dr. Sied Kebir
Dr. Manmeet Singh Ahluwalia
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 communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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

  • glioblastoma
  • molecular diagnostics
  • neuro-oncology
  • imaging biomarkers
  • immunotherapy
  • targeted therapy
  • tumor-treating fields
  • radiotherapy
  • chemotherapy
  • translational research
  • machine learning
  • positron emission tomography

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Review

22 pages, 1478 KB  
Review
Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment
by Reid Master, Nesha Rubin, James Sampson, Kamlesh K. Yadav, Shruti Pandita, Aria Sabbagh, Anika Krishnan, Patrick J. Silva, Kenneth S. Ramos, Vincent Gregoire, Nikos Paragios, Sunil Krishnan and Tej K. Pandita
Cancers 2025, 17(23), 3762; https://doi.org/10.3390/cancers17233762 - 25 Nov 2025
Viewed by 655
Abstract
Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability [...] Read more.
Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy planning guided by multimodal imaging and mathematical modeling. Studies have demonstrated reproducible tumor segmentations with DSCs exceeding 0.90, reduced planning within minutes, and emerging predictive capabilities for treatment response. Radiogenomic integration has enabled imaging-based classification of critical biomarkers with high accuracy, reinforcing the potential of deep learning models in personalized radiotherapy. Despite these innovations, deployment into clinical practice remains limited, primarily due to insufficient external validation and single-institution training datasets. This review emphasizes the importance of large, annotated imaging datasets, multi-institutional collaboration, and biologically explainable modeling to successfully translate deep learning into glioblastoma radiation planning and longitudinal monitoring. Full article
(This article belongs to the Special Issue Advances in Diagnostics and Treatments for Glioblastoma)
Show Figures

Figure 1

Back to TopTop