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Biomedicines
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Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies
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Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 6236

Special Issue Editor

Dr. Mubarak Taiwo Mustapha

E-Mail Website
Guest Editor
Operational Research Centre in Healthcare, Department of Biomedical Engineering, Near East University, Nicosia, Cyprus
Interests: AI-driven diagnostic tools; biomedical artificial intelligence; cancer detection algorithms; convolutional neural networks (CNNs); deep learning in healthcare; healthcare data analysis; interdisciplinary healthcare research; medical imaging analysis; precision medicine; transformer models in diagnostics

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies”, will collate cutting-edge research and comprehensive reviews focused on novel diagnostic methods for brain tumors. With the increasing application of artificial intelligence, machine learning, and advanced imaging technologies, the Special Issue aims to highlight the progress made in early detection, classification, and prognostication of brain tumors, including gliomas, meningiomas, and other neuro-oncological conditions.

We invite submissions that explore the integration of deep learning, medical imaging analysis, predictive modeling, and multi-modal data fusion for precise and reliable diagnostics. Topics may include (but are not limited to) innovative AI architectures, clinical data integration, and digital pathology approaches that enhance the accuracy of diagnosis and supports clinicians in their decision making. Additionally, contributions addressing challenges such as data scarcity, model interpretability, and clinical translation are highly welcome. This Special Issue aspires to provide a comprehensive overview of current advancements and offer a roadmap for future breakthroughs in brain tumor diagnostics, ultimately supporting better patient outcomes and personalized treatment pathways.

Dr. Mubarak Taiwo Mustapha
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 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. Biomedicines is an international peer-reviewed open access monthly 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

  • brain tumor diagnosis
  • artificial intelligence in oncology
  • deep learning in medical imaging
  • machine learning models
  • predictive analytics in healthcare
  • digital pathology
  • glioma detection
  • multi-modal data integration
  • neuro-oncology
  • precision medicine

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

Published Papers (4 papers)

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Research

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12 pages, 1315 KB  
Article
Longitudinal Cerebral Structural, Microstructural, and Functional Alterations After Brain Tumor Surgery for Early Detection of Recurrent Tumors
by Rebecca Kassubek, Mario Amend, Heiko Niessen, Bernd Schmitz, Jens Engelke, Nadja Grübel, Jochen Weishaupt, Karl Georg Haeusler, Jan Kassubek and Hans-Peter Müller
Biomedicines 2025, 13(11), 2811; https://doi.org/10.3390/biomedicines13112811 - 18 Nov 2025
Viewed by 461
Abstract
Background: Early detection of recurrent brain tumors after malignant glioma surgery is a challenge in imaging-based assessment of glioma. Objective: The aim of this case series is to investigate whether there are signs for an improvement in the early detection of [...] Read more.
Background: Early detection of recurrent brain tumors after malignant glioma surgery is a challenge in imaging-based assessment of glioma. Objective: The aim of this case series is to investigate whether there are signs for an improvement in the early detection of recurrent tumors using multiparametric magnetic resonance imaging (MRI) after glioma surgery. Methods: An MRI protocol was used with high-resolution fluid-attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), resting state functional MRI (rsfMRI), and contrast-enhanced high resolution T1-weighted (T1w). Longitudinal multiparametric MRI was performed in six patients with glioblastoma with one complete scan before surgery, one scan after surgery and at least two follow-up scans. A total of 27 complete multiparametric MRI data sets were available. Results: DTI analysis at the localizations of recurrent tumors showed early directionality loss in DTI by fractional anisotropy (FA) reduction accompanied by FLAIR hyperintensities before hyperintensities in contrast enhanced T1w were visible. One out of six patients showed a regional FA decrease at the localization of the recurrent tumor at a point of time even when the morphological T1w- and FLAIR images did not demonstrate any detectable changes. Functional connectivity alterations in a corresponding network could also be detected at the localizations of the recurrent tumor. Conclusions: In addition to routine T2w FLAIR and contrast enhanced T1w, DTI and rsfMRI might complement information for the early detection of recurrent malignant glioma. Prospective studies at larger scale are needed with respect to potential of DTI and rsfMRI for early recurrent tumor detection. Full article
(This article belongs to the Special Issue Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies)
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21 pages, 3605 KB  
Article
Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN
by Niraj Anil Babar, Junayd Lateef, ShahNawaz Syed, Julia Dietlmeier, Noel E. O’Connor, Gregory B. Raupp and Andreas Spanias
Biomedicines 2025, 13(10), 2571; https://doi.org/10.3390/biomedicines13102571 - 21 Oct 2025
Viewed by 1313
Abstract
Background/Objectives: Brain tumors arise from abnormal, uncontrolled cell growth due to changes in the DNA. Magnetic Resonance Imaging (MRI) is vital for early diagnosis and treatment planning. Artificial intelligence (AI), especially deep learning, has shown strong potential in assisting radiologists with MRI analysis. [...] Read more.
Background/Objectives: Brain tumors arise from abnormal, uncontrolled cell growth due to changes in the DNA. Magnetic Resonance Imaging (MRI) is vital for early diagnosis and treatment planning. Artificial intelligence (AI), especially deep learning, has shown strong potential in assisting radiologists with MRI analysis. However, many brain tumor classification models achieve high accuracy at the cost of large model sizes and slow inference, limiting their practicality for medical edge computing. In this work we introduce a new attention-guided classification model and explore how model parameters can be reduced without significantly impacting accuracy. Methods: We develop a shallow attention-guided convolutional neural network (ANSA_Ensemble) and evaluate its effectiveness using Monte Carlo simulations, ablation studies, cross-dataset generalization, and Grad-CAM-generated heatmaps. Several state-of-the-art model compression techniques are also applied to improve the efficiency of our classification pipeline. The model is evaluated on three open-source brain tumor datasets. Results: The proposed ANSA_Ensemble model achieves a best accuracy of 98.04% and an average accuracy of 96.69 ± 0.64% on the Cheng dataset, 95.16 ± 0.33% on the Bhuvaji dataset, and 95.20 ± 0.40% on the Sherif dataset. Conclusions: The performance of the proposed model is comparable to state-of-the-art methods. We find that the best tradeoff between accuracy and speed-up factor is consistently achieved using depthwise separable convolutions. The ablation study confirms the effectiveness of the introduced attention blocks and shows that model accuracy improves as the number of attention blocks increases. Our code is made publicly available. Full article
(This article belongs to the Special Issue Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies)
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19 pages, 6998 KB  
Article
Nanopore Sequencing Reveals Novel Alternative Splice Variants of EZH2 in Pediatric Medulloblastoma
by Josselen Carina Ramírez-Chiquito, Sergio Antony Rosete-Ambriz, Ana Consuelo Olguín-García, María del Pilar Eguía-Aguilar, Ana Maria Niembro-Zuñiga, Alfonso Marhx-Bracho, Mario Perezpeña-Diazconti and Sergio Juárez-Méndez
Biomedicines 2025, 13(10), 2461; https://doi.org/10.3390/biomedicines13102461 - 10 Oct 2025
Viewed by 797
Abstract
Background: Medulloblastoma is the childhood tumor with the highest morbidity and mortality worldwide. This type of cancer is characterized by a high degree of heterogeneity that gives rise to different molecular groups with disparities in the clinical presentation and prognosis. Among the molecular [...] Read more.
Background: Medulloblastoma is the childhood tumor with the highest morbidity and mortality worldwide. This type of cancer is characterized by a high degree of heterogeneity that gives rise to different molecular groups with disparities in the clinical presentation and prognosis. Among the molecular differences, one of the most relevant factors is alternative splicing, as it is responsible for transcriptomic diversity. EZH2 is a gene processed by alternative splicing that functions as an epigenetic regulator. In cancer, certain EZH2 mRNA variants are associated with tumorigenesis; however, in medulloblastoma, the alternative splicing pattern of EZH2 has not been studied. Currently, the best tool for identifying alternative splicing variants is long-read sequencing. Methods: We amplified the most variable region of EZH2 alternative splicing and used nanopore sequencing to obtain the transcriptional profile of the gene in patients with medulloblastoma. We verified the variants identified with Sanger sequencing and digital RT–PCR. Finally, we studied the relationship between the expression levels and the clinical–biological characteristics of the patients. Results: We identified seven mRNA variants of EZH2 expressed in medulloblastoma patients, five of which had not been reported previously. In addition, high expression of the novel variant EZH2_RetI8 was associated with patient mortality (p < 0.05). Conclusions: This is the first evidence of the EZH2 mRNA variant profile in medulloblastoma, revealing seven alternative transcripts, one of which is associated with patient mortality. This is a clear example of the complexity of the transcriptome and how long-read sequencing can resolve alternative splicing patterns. Full article
(This article belongs to the Special Issue Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies)
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Review

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26 pages, 3179 KB  
Review
Glioblastoma: A Multidisciplinary Approach to Its Pathophysiology, Treatment, and Innovative Therapeutic Strategies
by Felipe Esparza-Salazar, Renata Murguiondo-Pérez, Gabriela Cano-Herrera, Maria F. Bautista-Gonzalez, Ericka C. Loza-López, Amairani Méndez-Vionet, Ximena A. Van-Tienhoven, Alejandro Chumaceiro-Natera, Emmanuel Simental-Aldaba and Antonio Ibarra
Biomedicines 2025, 13(8), 1882; https://doi.org/10.3390/biomedicines13081882 - 2 Aug 2025
Cited by 1 | Viewed by 2801
Abstract
Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by rapid progression, profound heterogeneity, and resistance to conventional therapies. This review provides an integrated overview of GBM’s pathophysiology, highlighting key mechanisms such as neuroinflammation, genetic alterations (e.g., EGFR, PDGFRA), the tumor microenvironment, [...] Read more.
Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by rapid progression, profound heterogeneity, and resistance to conventional therapies. This review provides an integrated overview of GBM’s pathophysiology, highlighting key mechanisms such as neuroinflammation, genetic alterations (e.g., EGFR, PDGFRA), the tumor microenvironment, microbiome interactions, and molecular dysregulations involving gangliosides and sphingolipids. Current diagnostic strategies, including imaging, histopathology, immunohistochemistry, and emerging liquid biopsy techniques, are explored for their role in improving early detection and monitoring. Treatment remains challenging, with standard therapies—surgery, radiotherapy, and temozolomide—offering limited survival benefits. Innovative therapies are increasingly being explored and implemented, including immune checkpoint inhibitors, CAR-T cell therapy, dendritic and peptide vaccines, and oncolytic virotherapy. Advances in nanotechnology and personalized medicine, such as individualized multimodal immunotherapy and NanoTherm therapy, are also discussed as strategies to overcome the blood–brain barrier and tumor heterogeneity. Additionally, stem cell-based approaches show promise in targeted drug delivery and immune modulation. Non-conventional strategies such as ketogenic diets and palliative care are also evaluated for their adjunctive potential. While novel therapies hold promise, GBM’s complexity demands continued interdisciplinary research to improve prognosis, treatment response, and patient quality of life. This review underscores the urgent need for personalized, multimodal strategies in combating this devastating malignancy. Full article
(This article belongs to the Special Issue Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies)
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