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Medicina
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Early Diagnosis and Management of Glioma
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Early Diagnosis and Management of Glioma

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1604

Special Issue Editors

Prof. Dr. Maurizio Iacoangeli

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Guest Editor
Department of Neurosurgery, Università Politecnica delle Marche, Azienda Ospedaliero Universitaria delle Marche, 60121 Ancona, Italy
Interests: neurosurgery
Dr. Denis Aiudi

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Guest Editor Assistant
Department of Neurosurgery, Università Politecnica delle Marche, Azienda Ospedaliero Universitaria delle Marche, 60121 Ancona, Italy
Interests: neurosurgery; neurology

Special Issue Information

Dear Colleagues,

Gliomas are the most common primary brain tumors in adults and include a series of highly variable histotypes with different clinical, prognostic, and therapeutic characteristics. All these tumors can benefit from multidisciplinary treatments, which involve the neurosurgical, radiological, oncology, and radiotherapy components, as well as the anatomopathological one.

The early diagnosis of the disease is based on rapid radiological findings, which allows us to advance the first diagnostic hypothesis and on which the indication for surgical treatment will then be advanced. Early diagnosis and surgical treatment play a fundamental role because they allow an early histological and biomolecular diagnosis, as well as rapid treatment of the tumor in its progress, which can therefore benefit from surgical removal as maximally as possible. This also allows the patient to be directed towards an oncological and radiotherapy path with a clinical status that can allow them to face the subsequent necessary therapies. In this scenario, the role of new diagnostic and therapeutic technologies is therefore fundamental, which can allow early treatment of gliomas at onset, consequently guaranteeing the most favorable prognosis possible.

In this Special Issue, we hope to be able to receive valuable scientific contributions that can confirm and implement this concept and that provide the basis for subsequent studies that have a concrete impact on daily clinical practice, which can guarantee a better prognosis for this type of patient.

Prof. Dr. Maurizio Iacoangeli
Guest Editor

Dr. Denis Aiudi
Guest Editor Assistant

Manuscript Submission Information

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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. Medicina 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 2200 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
  • glioblastoma
  • neuroimaging
  • neuro-oncology
  • early diagnosis
  • modern technologies
  • neurosurgery
  • tumor biology
  • early therapy
  • new therapies

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Published Papers (3 papers)

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Research

13 pages, 2605 KiB  
Article
Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment
by Yan Qin, Wei You, Yulong Wang, Yu Zhang, Zhijie Xu, Qingling Li, Yuelong Zhao, Zhiwei Mou and Yitao Mao
Medicina 2025, 61(6), 1034; https://doi.org/10.3390/medicina61061034 - 3 Jun 2025
Viewed by 266
Abstract
Background and Objectives: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies have primarily focused on [...] Read more.
Background and Objectives: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies have primarily focused on the binary classification (i.e., high grade vs. low grade) of gliomas. In order to perform the four-grade (grades I, II, III, and IV) glioma classification preoperatively, we constructed an artificial neural network (ANN) model using magnetic resonance imaging data. Materials and Methods: We reviewed and included patients with gliomas who underwent preoperative MRI examinations. Radiomics features were derived from contrast-enhanced T1-weighted images (CE-T1WI) using Pyradiomics and were selected based on their Spearman’s rank correlation with glioma grades. We developed an ANN model to classify the four pathological grades of glioma, assigning training and validation sets at a 3:1 ratio. A diagnostic confusion matrix was employed to demonstrate the model’s diagnostic performance intuitively. Results: Among the 362-patient cohort, the ANN model’s diagnostic performance plateaued after incorporating the first 19 of the 530 extracted radiomic features. At this point, the average overall diagnostic accuracy ratings for the training and validation sets were 91.28% and 87.04%, respectively, with corresponding coefficients of variation (CVs) of 0.0190 and 0.0272. The diagnostic accuracies for grades I, II, III, and IV in the training set were 91.9%, 89.9%, 92.1%, and 90.7%, respectively. The diagnostic accuracies for grades I, II, III, and IV in the validation set were 88.7%, 87.1%, 86.5%, and 86.9%, respectively. Conclusions: The MRI radiomics-based ANN model shows promising potential for the four-type classification of glioma grading, offering an objective and noninvasive method for more refined glioma grading. This model could aid in clinical decision making regarding the treatment of patients with various grades of gliomas. Full article
(This article belongs to the Special Issue Early Diagnosis and Management of Glioma)
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12 pages, 3566 KiB  
Article
The Impact of Coagulation Biomarkers on Survival Outcomes in Adult Glioblastoma
by Rahmi Atıl Aksoy, Timur Koca, Yasemin Şengün, Ece Atak and Aylin Fidan Korcum
Medicina 2025, 61(4), 756; https://doi.org/10.3390/medicina61040756 - 19 Apr 2025
Viewed by 372
Abstract
Background and Objectives: Glioblastoma presents a significant challenge in oncology due to its aggressive nature and poor prognosis, despite advancements in treatment. This study aims to comprehensively evaluate the prognostic significance of coagulation biomarkers, including the novel albumin/D-dimer ratio, in adult glioblastoma [...] Read more.
Background and Objectives: Glioblastoma presents a significant challenge in oncology due to its aggressive nature and poor prognosis, despite advancements in treatment. This study aims to comprehensively evaluate the prognostic significance of coagulation biomarkers, including the novel albumin/D-dimer ratio, in adult glioblastoma patients. Material and Methods: This retrospective study included 74 adult glioblastoma patients who underwent Stupp protocol treatment. Blood samples were collected before radiotherapy to measure biomarkers, including prothrombin time (PT), activated partial thromboplastin time (aPTT), albumin, D-dimer, and the albumin/D-dimer ratio. The prognostic significance of these biomarkers for progression-free survival (PFS) and overall survival (OS) was assessed using both univariate and multivariate Cox regression analyses. Results: The median follow-up time was 12.2 months (range, 1–77.4 months). Univariate analysis revealed that ECOG performance status (p = 0.001), D-dimer (p = 0.03), and albumin (p = 0.001) were significant prognostic factors for PFS. Multivariate analysis identified albumin (p = 0.02) as an independent prognostic biomarker for PFS. For OS, univariate analysis showed that age (p = 0.004), ECOG performance status (p = 0.001), tumor volume (p = 0.007), extent of resection (p = 0.01), radiotherapy dose (p = 0.001), D-dimer (p = 0.02), albumin (p = 0.001), albumin/D-dimer ratio (p = 0.02), and PT (p = 0.002) were significant prognostic factors. Multivariate analysis revealed age (p = 0.04), extent of resection (p = 0.02), and PT (p = 0.04) as independent prognostic factors for OS. Conclusions: Our findings highlight the prognostic significance of coagulation biomarkers, particularly PT, D-dimer, albumin, and the albumin/D-dimer ratio, in glioblastoma. These biomarkers may serve as valuable tools for prognostic assessment and personalized treatment strategies, warranting further exploration in larger prospective studies. Full article
(This article belongs to the Special Issue Early Diagnosis and Management of Glioma)
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13 pages, 1127 KiB  
Article
Analysis of Patients with Glioblastoma Treated with Standard 6-Week Chemoradiation Followed by Temozolomide: Treatment Outcomes and Prognostic Factors
by Sojung Lee and Myungsoo Kim
Medicina 2025, 61(3), 376; https://doi.org/10.3390/medicina61030376 - 21 Feb 2025
Viewed by 646
Abstract
Background and Objectives: We aimed to investigate the treatment outcomes and prognostic factors of survival among patients with glioblastoma who underwent 6-week concurrent chemoradiation therapy (CCRT) followed by temozolomide (TMZ) with Stupp’s regimen in a single tertiary institution. Materials and Methods: [...] Read more.
Background and Objectives: We aimed to investigate the treatment outcomes and prognostic factors of survival among patients with glioblastoma who underwent 6-week concurrent chemoradiation therapy (CCRT) followed by temozolomide (TMZ) with Stupp’s regimen in a single tertiary institution. Materials and Methods: Eighty patients with glioblastoma who underwent 6-week CCRT followed by TMZ between June 2010 and January 2024 were retrospectively investigated. A survival analysis was performed of factors such as age, O (6)-methylguanine-DNA methyltransferase promoter (MGMT) methylation, extent of resection, pre- and post-operative Karnofsky Performance Status, and inflammatory markers such as neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio. Post-operative inflammatory markers were assessed at 2–3 weeks post-operative before the initiation of CCRT. A subgroup analysis was performed of patients who underwent non-gross total resection (GTR). Results: The median progression-free survival (PFS) and overall survival (OS) of the entire cohort were 8.97 months and 19.0 months, respectively. Older age (≥65 years) and non-GTR status were adverse prognostic factors of PFS and OS. MGMT methylation is a favorable prognostic factor for PFS and OS. In the subgroup of patients who underwent non-GTR, MGMT methylation and post-operative LMR (<3.2/>3.2) were independent prognostic factors for PFS and OS. Conclusions: As with previous studies, older age, MGMT methylation, and extent of resection were independent prognostic factors for the survival of patients with glioblastoma who underwent standard treatment with Stupp’s regimen. MGMT methylation and post-operative LMR were significant prognostic factors for PFS and OS among patients who underwent non-GTR. The prognostic significance of post-operative inflammatory markers for treatment response and survival should be further validated in glioblastoma patients treated with Stupp’s regimen. Full article
(This article belongs to the Special Issue Early Diagnosis and Management of Glioma)
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